Erosion Productivity Impact Calculator Research Articles

High-resolution digital mapping of soil erodibility in China

Soil erodibility (K) is the intrinsic susceptibility of a soil to water erosion. Currently, its detailed and accurate spatial distribution information especially over large areas is urgently required for national and regional soil erosion assessment and soil conservation decision making. This study combined pedotransfer function with digital soil mapping techniques to develop a high-resolution map of soil erodibility across China. The First, based on a recent national soil survey, we adopted the erosion-productivity impact calculator (EPIC) to calculate soil erodibility values at 4710 soil sampling points. Then, with the caclulated values of points, we used five techniques including polygon linking (PL), ordinary kriging (OK), Cubist, extreme gradient boosting (XGBoost), and random forests (RF) to generate spatial distribution of soil erodibility. The three latter machine learning techniques modeled the quantitative relationships between soil erodibility and a set of environmental covariates. The results showed that machine learning methods exhibited much more spatial details than the PL and OK did. Among the five techniques the RF achieved the highest accuracy with R2 of 0.49 and RMSE of 0.0077 t ha h ha−1 MJ−1 mm−1 based on 10-fold cross-validation. Spatial uncertainty analysis of the RF predictions showed that high uncerntainty occurred in northwestern China and low uncertainty in the center and southeast. We found that topographical and climatic variables are major environmental factors indirectly controlling spatial variation of soil erodibility while the soil particle composition and SOC contents directly influence the variation.

Divergent effectiveness of irrigation in enhancing food security in droughts under future climates with various emission scenarios

Food security is a key target in the UN Sustainable Development Goals (SDGs) and is also one of the biggest challenges for China, the largest developing country in population in the world. Massive attention has been directed to the future impacts of hydrometeorological extremes on crop yield. However, knowledge gaps still stand concerning the effectiveness of irrigation, as the largest water consumption sector, on agricultural production under different climate scenarios. Here we showed the drought based on the modified Palmer Drought Severity Index and analyzed the drought-induced wheat yield losses in irrigated and non-irrigated agriculture across China according to three methods including the Multiple Linear Regression method, Deep Learning algorithm, and Erosion-Productivity Impact Calculator model. We found that the droughts become more intensive in the future, and drought-induced wheat yield loss under RCP8.5 scenario was expected to reach 32–49%. Intercomparison of drought-induced wheat yield changes between irrigated and non-irrigated agriculture indicated significantly less drought-induced crop-yield losses given sufficient irrigation under the RCP2.6 and RCP6.0 scenarios. However, our results under RCP8.5 suggested that the effectiveness of irrigation in enhancing food security is minor under this high-emission future climate scenario. These findings allowed us to revisit the effectiveness of irrigation in a warming climate and highlighted the importance of climate change mitigation in food security.

Effects of land use types on soil erodibility in a small karst watershed in western Hubei.

Soil erosion is a severe problem in the karst watershed, and analysis of soil erosion at the watershed scale is urgently needed. This study tried to estimate the soil erodibility factor (K-factor) using the Erosion Productivity Impact Calculator (EPIC) nomograph and evaluate the spatial distribution of the predicted K-factor in a karst watershed. Soil properties and K-factors of five land use types (NF: natural mixed forest, CF: cypress forest, EF: economic forest, ST: stone dike terrace, VF: vegetable land) in the Xialaoxi small watershed were compared and key factors affecting erodibility were analyzed. Results showed that (1) The erodibility K-factor was unevenly distributed within different site types and strongly influenced by anthropogenic activities. The soil K-factors of sample sites subjected to frequent human disturbance (ST, VF) were high, ranging from 0.0480-0.0520 thm2h/(MJmmhm2), while the soil K-factors of natural site types (NF, CF, and EF) were low, ranging from 0.0436-0.0448 thm2h/(MJmmhm2). (2) The soil texture in the Xialaoxi watershed was mostly loamy, and that of the agricultural areas frequently disturbed by agricultural practices (ST, VF) was silty loam. (3) Soil carbon fractions were affected by land use types. Soil organic carbon storage of NF and CF had strong spatial heterogeneity. The soil organic carbon (SOC) and labile organic carbon (LOC) of the two were significantly higher than those of the disturbed EF and cultivated land soil. (4) There was a synergistic effect between the soil properties and the K-factor. K was significantly negatively related to sand fractions (2-0.05 mm) and non-capillary porosity, while positively related to silt content (0.05-0.002 mm). Overall, changes in bulk density (BD), total porosity (TP), non-capillary porosity (NCP), texture, and organic matter content caused by natural restoration or anthropogenic disturbance were the main reasons for soil erodibility. Natural care (sealing) and construction of stone dike planting practices were effective ways to reduce soil erosion in small karst watershed areas of western Hubei.

Comparison of the Applicability of Different Soil Erosion Models to Predict Soil Erodibility Factor and Event Soil Losses on Loess Slopes in Hungary

Climate change induces more extreme precipitation events, which increase the amount of soil loss. There are continuous requests from the decision-makers in the European Union to provide data on soil loss; the question is, which ones should we use? The paper presents the results of USLE (Universal Soil Loss Equation), RUSLE (Revised USLE), USLE-M (USLE-Modified) and EPIC (Erosion-Productivity Impact Calculator) modelling, based on rainfall simulations performed in the Koppány Valley, Hungary. Soil losses were measured during low-, moderate- and high-intensity rainfalls on cultivated soils formed on loess. The soil erodibility values were calculated by the equations of the applied soil erosion models and ranged from 0.0028 to 0.0087 t ha h ha−1 MJ−1 mm−1 for the USLE-related models. EPIC produced larger values. The coefficient of determination resulted in an acceptable correlation between the measured and calculated values only in the case of USLE-M. Based on other statistical indicators (e.g., NSEI, RMSE, PBIAS and relative error), RUSLE, USLE and USLE-M resulted in the best performance. Overall, regardless of being non-physically based models, USLE-type models seem to produce accurate soil erodibility values, thus modelling outputs.

Simulating soil erodibility in southeastern China using a sequential Gaussian algorithm

Soil erodibility (K) is a key factor of soil erosion, and its appropriate quantification and interpolation are vitally important for soil and water conservation. The traditional point-represent-polygon approaches and common kriging method for the estimation of K, however, do not sufficiently represent the original data. The objectives of this study were to simulate the spatial distribution of K using a sequential Gaussian algorithm and analyze the uncertainty in evaluating the risk of soil erodibility in southeastern China. We determined 101 sampling points in the area and collected disturbed soil samples from the 0–20 cm layer at each point. Soil properties were determined, and K was calculated using five common models: the EPIC (Erosion/Productivity Impact Calculator), approximate nomograph, Torri, Shirazi, and Wang models. Among the chosen models, the EPIC model performed the best at estimating K(KEPIC), which ranged from 0.019 to 0.060 t ha h (ha MJ mm)−1, with a mean of 0.043 t ha h (ha MJ mm)−1. The KEPIC was moderately spatially variable and had a limited spatial structure, increasing from south to north in our study area, and all spatial simulations using the cooperative kriging (CK) interpolation and the sequential Gaussian simulation (SGS) with 10, 25, 50, 100, 200, and 500 realizations had acceptable accuracies. The CK interpolation narrowed the range, and the SGS maintained the original characteristics of the calculated data. The proportions of the risk area were 38.0% and 10.1%, when the risk probability for K was 60% and 80%, respectively, and high risk areas were mostly located in the north. The results provide scientific guidance for managing the risk of soil erodibility in southeastern China.

Distribution of soil nutrients and erodibility factor under different soil types in an erosion region of Southeast China.

BackgroundSoil erosion can affect the distribution of soil nutrients, which restricts soil productivity. However, it is still a challenge to understand the response of soil nutrients to erosion under different soil types.MethodsThe distribution of soil nutrients, including soil organic carbon (SOC), soil organic nitrogen (SON), and soil major elements (expressed as Al2O3, CaO, Fe2O3, K2O, Na2O, MgO, TiO2, and SiO2), were analyzed in the profiles from yellow soils, red soils, and lateritic red soils in an erosion region of Southeast China. Soil erodibility K factor calculated on the Erosion Productivity Impact Calculator (EPIC) model was used to indicate erosion risk of surface soils (0∼30 cm depth). The relationships between these soil properties were explored by Spearman’s rank correlation analysis, further to determine the factors that affected the distribution of SOC, SON, and soil major elements under different soil types.ResultsThe K factors in the red soils were significantly lower than those in the yellow soils and significantly higher than those in the lateritic red soils. The SON concentrations in the deep layer of the yellow soils were twice larger than those in the red soils and lateritic red soils, while the SOC concentrations between them were not significantly different. The concentrations of most major elements, except Al2O3 and SiO2, in the yellow soils, were significantly larger than those in the red soils and lateritic red soils. Moreover, the concentrations of major metal elements positively correlated with silt proportions and SiO2 concentrations positively correlated with sand proportions at the 0∼80 cm depth in the yellow soils. Soil major elements depended on both soil evolution and soil erosion in the surface layer of yellow soils. In the yellow soils below the 80 cm depth, soil pH positively correlated with K2O, Na2O, and CaO concentrations, while negatively correlated with Fe2O3 concentrations, which was controlled by the processes of soil evolution. The concentrations of soil major elements did not significantly correlate with soil pH or particle distribution in the red soils and lateritic red soils, likely associated with intricate factors.ConclusionsThese results suggest that soil nutrients and soil erodibility K factor in the yellow soils were higher than those in the lateritic red soils and red soils. The distribution of soil nutrients is controlled by soil erosion and soil evolution in the erosion region of Southeast China.

Comparative analysis of soil nutrients under different land-use types in the Mun River basin of Northeast Thailand

Land-use/cover management is one of the driven forces to induce soil erosion. Meanwhile, the loss of soil nutrients caused by soil erosion severely restricts soil fertility. However, the differences in soil nutrients under different land-use types in an erodible environment are complex and the factors are still unclear in tropical river basin. In the Mun River basin of Northeast Thailand, a total of six soil profiles were selected from the native forest, artificial forest, paddy land, and abandoned paddy lands with 1, 3, and 5-year abandoned history, respectively. The contents of soil nutrients, including soil organic carbon (SOC), soil organic nitrogen (SON), and inorganic minerals (Al2O3, CaO, Fe2O3, K2O, MgO, and MnO), in the soil profiles were analyzed to determine their distribution under different land-use types. Soil erodibility K factor was calculated by the erosion productivity impact calculator (EPIC) model to estimate the potential of soil erosion under different land-use types. Moreover, the relationships between soil nutrients and the K factor were determined by linear regression analysis and Spearman’s rank correlation analysis. The silty soils under artificial forest were significantly larger for the contents of SOC, SON, Al2O3, Fe2O3, and MnO than the sandy soils under native forest, which was mainly attributed to the abundant fine particles in the silty soils. Fine particles played a key role in being the main carriers of inorganic minerals and combining with organic matter. The contents of SOC, SON, and all inorganic minerals in the soils of the abandoned paddy lands were significantly lower than those in the paddy field, resulting from the severe loss of fine particles under intense soil erosion. The soils with abundant fine particles under the native forest land and paddy land had significantly higher K factors compared to the coarse-textured soils under other land-use types. The K factor was mainly controlled by the proportion of fine particles in the Mun River basin. The paddy field easily occurred severe soil erosion at the early stage (1 year) of paddy land abandonment due to the lack of protective management, which caused decreasing SOC, SON, and inorganic minerals with the loss of fine particles. However, the K2O, MgO, and CaO contents significantly increased after 3 ~ 5 years of paddy land abandonment through the supplement from high salinity groundwater. These results suggest that continual paddy cultivation can effectively maintain soil nutrients in the sandy soils of the tropical river basin.

Assessing soil degradation under land-use change: insight from soil erosion and soil aggregate stability in a small karst catchment in southwest China.

BackgroundSoil erodibility (K factor) and soil aggregate stability are often used to assess soil degradation in an erodible environment. However, their applicability under land-use change is uncertain, especially agricultural abandonment.MethodsDifferent land-use types, including cropland, abandoned cropland, and native vegetation land, were converted into the successive stages following agricultural abandonment by space-for-time substitution approach in a small karst catchment, Southwest China. The indexes of soil aggregate stability and K factor of the Erosion Productivity Impact Calculator (EPIC) model in soil profiles were calculated to identify which method is suitable to indicate soil degradation under land-use change.ResultsThe indexes of soil aggregate stability in the soils at 0∼30 cm depth under native vegetation land were significantly larger than those under cropland and slightly larger than those under abandoned cropland. The K factor was not significantly different among the three land use examples because the EPIC model does not consider soil permeability. In the soil organic carbon (SOC)-rich soils (>2%), the K factor was significantly correlated with silt and clay content ranging within a narrow scope of near 0.010 t hm2 h/hm2/MJ/mm. While in the SOC-poor soils, the K factor was significantly increased with decreasing SOC content and was significantly correlated with soil aggregate stability.ConclusionsSoil aggregate stability is more suitable to indicate soil degradation under land-use change. Sufficient SOC in erodible soils would restrain soil degradation, while SOC loss can significantly increase soil erosion risk.

Changes in soil properties and erodibility of gully heads induced by vegetation restoration on the Loess Plateau, China

Soil erosion on the Loess Plateau of China is effectively controlled due to the implementation of several ecological restoration projects that improve soil properties and reduce soil erodibility. However, few studies have examined the effects of vegetation restoration on soil properties and erodibility of gully head in the gully regions of the Loess Plateau. The objectives of this study were to quantify the effects of vegetation restoration on soil properties and erodibility in this region. Specifically, a control site in a slope cropland and 9 sites in 3 restored land-use types (5 sites in grassland, 3 in woodland and 1 in shrubland) in the Nanxiaohegou watershed of a typical gully region on the Loess Plateau were selected, and soil and root samples were collected to assess soil properties and root characteristics. Soil erodibility factor was calculated by the Erosion Productivity Impact Calculator method. Our results revealed that vegetation restoration increased soil sand content, soil saturated hydraulic conductivity, organic matter content and mean weight diameter of water-stable aggregate but decreased soil silt and clay contents and soil disintegration rate. A significant difference in soil erodibility was observed among different vegetation restoration patterns or land-use types. Compared with cropland, soil erodibility decreased in the restored lands by 3.99% to 21.43%. The restoration patterns of Cleistogenes caespitosa K. and Artemisia sacrorum L. in the grassland showed the lowest soil erodibility and can be considered as the optimal vegetation restoration pattern for improving soil anti-erodibility of the gully heads. Additionally, the negative linear change in soil erodibility for grassland with restoration time was faster than those of woodland and shrubland. Soil erodibility was significantly correlated with soil particle size distribution, soil disintegration rate, soil saturated hydraulic conductivity, water-stable aggregate stability, organic matter content and root characteristics (including root average diameter, root length density, root surface density and root biomass density), but it showed no association with soil bulk density and soil total porosity. These findings indicate that although vegetation destruction is a short-term process, returning the soil erodibility of cropland to the level of grassland, woodland and shrubland is a long-term process (8–50 years).

Distribution of Shrubland and Grassland Soil Erodibility on the Loess Plateau.

Soil erosion is one of the most severe problems facing environments and has increased throughout the 20th century. Soil erodibility (K-factor) is one of the important indicators of land degradation, and many models have been used to estimate K values. Although soil erodibility has been estimated, the comparison of different models and their usage at a regional scale and, in particular, for different land use types, need more research. Four of the most widely distributed land use types were selected to analyze, including introduced and natural grassland, as well as introduced and natural shrubland. Soil particle size, soil organic matter and other relevant soil properties were measured to estimate soil erodibility in the Loess Plateau. The results show that: (1) the erosion productivity impact calculator (EPIC) model and SHIRAZI model are both suitable for the Loess Plateau, while the SHIRAZI model has the advantage of fewer parameters; (2) introduced grassland has better ability to protect both the 0–5 cm soils and 5–20 cm soils, while the differences between introduced and natural shrubland are not obvious at a catchment scale; (3) the K values of introduced grassland, natural grassland, introduced shrubland and natural shrubland in the 0–5 cm layer vary from 0.008 to 0.037, 0.031 to 0.046, 0.012 to 0.041 and 0.008 to 0.045 (t·hm2·h/(MJ·mm·hm2)), while the values vary from 0.009 to 0.039, 0.032 to 0.046, 0.012 to 0.042 and 0.008 to 0.048 (t·hm2·h/(MJ·mm·hm2)) in the 5–20 cm layer. The areas with a mean multiyear precipitation of 370–440 mm are the most important places for vegetation restoration construction management at a regional scale. A comprehensive balance between water conservation and soil conservation is needed and important when selecting the species used to vegetation restoration. This study provides suggestions for ecological restoration and provides a case study for the estimate of soil erodibility in arid and semiarid areas.

Biological soil crusts decrease erodibility by modifying inherent soil properties on the Loess Plateau, China

Soil erosion and subsequent land degradation contributed to societal collapse in the past and are a leading cause of desertification in arid and semi-arid regions. Biological soil crusts (biocrusts) are ubiquitous living covers in many arid and semiarid ecosystems that have an important role in soil stabilization and erosion prevention. The “Grain for Green” ecological project improved vegetation recovery, and led to an extensive development of biocrusts across the Loess Plateau region in China, one of the most eroded regions in the world. The expansion of biocrusts was instrumental in reducing soil loss in a very large, severely eroded region of the Loess Plateau. We hypothesized that development of biocrusts would change soil organic matter (SOM) and soil particle size distribution (PSD), thereby reducing soil erodibility and soil loss. We sampled 56 sites that were passively revegetated grasslands on former croplands and 3 bare soil sites in the Loess Plateau region, and used the erosion productivity impact calculator (EPIC) model combined with simulated rainfall to test our assumption. The PSD and SOM content varied significantly among biocrust types and successional stages. The SOM content was 4 times higher in moss dominated biocrust and 1.5 times greater in cyanobacterially dominated biocrust than bare soil. More fine-particles (< 0.01 mm) and fewer coarse-particles (0.05–0.25 mm) were present in biocrusts than in bare soil. Modeled soil erodibility decreased significantly as biocrust biomass increased, mainly due to increase in SOM content, reducing the predicted soil loss by up to 90%. Finally, the prevalence of moss biocrust was a better predictor of soil erodibility than cyanobacteria in the Loess Plateau region. We conclude that biocrusts were a decisive factor for the initial reduction of soil erosion, which must be considered explicitly in models that aim to predict and manage soil loss on the Loess Plateau.

Improved USLE-K factor prediction: A case study on water erosion areas in China

Soil erodibility (K-factor) is an essential factor in soil erosion prediction and conservation practises. The major obstacles to any accurate, large-scale soil erodibility estimation are the lack of necessary data on soil characteristics and the misuse of variable K-factor calculators. In this study, we assessed the performance of available erodibility estimators Universal Soil Loss Equation (USLE), Revised Universal Soil Loss Equation (RUSLE), Erosion Productivity Impact Calculator (EPIC) and the Geometric Mean Diameter based (Dg) model for different geographic regions based on the Chinese soil erodibility database (CSED). Results showed that previous estimators overestimated almost all K-values. Furthermore, only the USLE and Dg approaches could be directly and reliably applicable to black and loess soil regions. Based on the nonlinear best fitting techniques, we improved soil erodibility prediction by combining Dg and soil organic matter (SOM). The NSE, R2 and RE values were 0.94, 0.67 and 9.5% after calibrating the results independently; similar model performance was showed for the validation process. The results obtained via the proposed approach were more accurate that the former K-value predictions. Moreover, those improvements allowed us to effectively establish a regional soil erodibility map (1:250,000 scale) of water erosion areas in China. The mean K-value of Chinese water erosion regions was 0.0321(thah)·(haMJmm)−1 with a standard deviation of 0.0107(thah)·(haMJmm)−1; K-values present a decreasing trend from North to South in water erosion areas in China. The yield soil erodibility dataset also satisfactorily corresponded to former K-values from different scales (local, regional, and national).

Risk assessment of maize drought disaster in southwest China using the Environmental Policy Integrated Climate model

The East Asian monsoon has a tremendous impact on agricultural production in China. An assessment of the risk of drought disaster in maize-producing regions is therefore important in ensuring a reduction in such disasters and an increase in food security. A risk assessment model, EPIC (Environmental Policy Integrated Climate) model, for maize drought disasters based on the Erosion Productivity Impact Calculator crop model is proposed for areas with the topographic characteristics of the mountainous karst region in southwest China. This region has one of the highest levels of environmental degradation in China. The results showed that the hazard risk level for the maize zone of southwest China is generally high. Most hazard index values were between 0.4 and 0.5, accounting for 47.32% of total study area. However, the risk level for drought loss was low. Most of the loss rate was <0.1, accounting for 96.24% of the total study area. The three high-risk areas were mainly distributed in the parallel ridge–valley areas in the east of Sichuan Province, the West Mountain area of Guizhou Province, and the south of Yunnan Province. These results provide a scientific basis and support for the reduction of agricultural drought disasters and an increase in food security in the southwest China maize zone.

Impact of the Agricultural Research Service Watershed Assessment Studies on the Conservation Effects Assessment Project Cropland National Assessment

WATERSHED MODELING AND USDA CONSERVATION POLICY PLANNING The Soil and Water Resources Conservation Act (RCA) of 1977 provides the USDA broad strategic assessment and planning authority for the conservation, protection, and enhancement of soil, water, and related natural resources (USDA NRCS 2011). Through RCA, USDA WATERSHED MODELING AND USDA CONSERVATION POLICY PLANNING The 1985 RCA Appraisal was the first to use a comprehensive model (EPIC; Erosion Productivity Impact Calculator) to estimate the impact of soil erosion on crop productivity (Williams et al. 1984). EPIC is a field-scale model, and thousands of representative fields were modeled across the agricultural regions of the continental United States. The first watershed-based RCA assessment, called the Hydrologic Unit Model for the United States (HUMUS), was undertaken for the 1997 RCA Appraisal (Srinivasan et al. 1998; Arnold et al. 1999). HUMUS provides the necessary technical basis that enables the status of the nation's water resources to be determined at the national scale. The HUMUS framework enables modeling of spatially…

Evaluation of Erosion Productivity Impact Calculator (EPIC) Model for Nitrogen Losses in Rice Paddy of Thailand

Evaluation of Erosion Productivity Impact Calculator (EPIC) Model for Nitrogen Losses in Rice Paddy of Thailand

Volume-based soil particle fractal relation with soil erodibility in a small watershed of purple soil

The particle size distribution in small watershed changes under different land uses and affects soil erodibility. The aims of this study were (1) to investigate the volume fractal dimension of particle size distribution under different land uses in a typical small watershed of purple soil, (2) to estimate soil erodibilities of various land uses utilizing the Erosion-Productivity Impact Calculator (EPIC) model and the nomogram (NOMO) model, and (3) to relate volume fractal dimension with the soil erodibility used in the Universal Soil Loss Equation (USLE) in purple soil areas. Laser diffractions and double-logarithmic model were used to measure and calculate volume fractal dimension values. The results show that soil volume fractal dimensions were well linearly fitted to the double-logarithmic model with high correlation coefficients of 0.902–0.936 under six land uses in the small watershed. The averaged volume fractal dimension values under different land uses, from high to low were in the order of Zea mays L, Ipomoea batatas, Citrus reticulata Blanco, Setaria viridis, Robinia pseudoacacia L, Pinus massoniana Lamb. The volume fractal dimension was positively correlated to clay particle fraction (R = 0.933). The average soil erodibility values under different land uses from high to low were in the order of Setaria viridis, Citrus reticulata Blanco, Pinus massoniana Lamb, Zea mays L, Ipomoea batatas, Robinia pseudoacacia L while average soil erodibilities from high to low values were in the order of Setaria viridis, Citrus reticulata Blanco, Zea mays L, Ipomoea batatas, Pinus massoniana Lamb, Robinia pseudoacacia L. The soil erodibilities calculated by the two models were similar, and positively correlated (R = 0.630–0.877). The volume fractal dimension values of six land uses were negatively correlated to both soil erodibility estimated by EPIC and by NOMO models. Moreover, the correlations of the volume fractal dimension values of Zea mays L, Ipomoea batatas and Citrus reticulata Blanco estimated by EPIC or NOMO were lower than those of Pinus massoniana Lamb, Robinia pseudoacacia L and Setaria viridis. Further research is needed to determine the influence of volume fractal dimension on the soil erodibility under different land use and managements.

Comparison of soil erodibility factors in USLE, RUSLE2, EPIC and Dg models based on a Chinese soil erodibility database

Soil erodibility (K-value) is a key parameter in erosion prediction and is important for conservation planning in the face of a rising need for protecting the limited land resources. This study investigated the predictive capability of the K-value estimated by Universal Soil Loss Equation (USLE), Revised Universal Soil Loss Equation (RUSLE), Erosion Productivity Impact Calculator (EPIC) and Dg models for different soil regions using a Chinese soil erodibility database covering 51 natural runoff plots. Model performance was evaluated using R 2 (coefficient of determination), relative error (RE), Nash–Sutcliff efficiency (NSE) and P value (Mann–Whitney U test) statistics. The results showed that the existing four models overestimated almost all the K-values for the Chinese erodibility database, with most observed values concentrated in the range of 0.015–0.035. Without calibration, only the USLE and Dg models could be reliable and directly applied for the black soil region and the loess soil region, respectively. The Dg–OM model (R 2=0.67, n=32) was established by the non-linear best fitting techniques of multiple regression. In the Dg–OM model, K-values accounted for the vibration in a combination of the D g (geometric mean diameter) and OM (soil organic matter). NSE, R 2 and the average RE was 0.94, 0.67 and 9.5% for the Dg–OM model's calibration based on the Chinese erodibility database; similar results were found for the validation process, with NSE of 0.93, R 2 of 0.66 and average RE of 6.5%. The model performances showed that the Dg–OM model reached ‘good’ satisfactory level. Compared with the four existing erodibility models, the Dg–OM model permitted the best parameterization and accuracy, and was proved to be suitable for estimating soil erodibility values in China.

Maize drought disaster risk assessment of China based on EPIC model

Digital Agriculture is one of the important applications of Digital Earth. As the global climate changes and food security becomes an increasingly important issue, agriculture drought comes to the focus of attention. China is a typical monsoon climate country as well as an agricultural country with the world's largest population. The East Asian monsoon has had a tremendous impact upon agricultural production. Therefore, a maize drought disaster risk assessment, in line with the requirements of sustainable development of agriculture, is important for ensuring drought disaster reduction and food security. Meteorology, soil, land use, and agro-meteorological observation information of the research area were collected, and based on the concept framework of ‘hazard-inducing factors assessment (hazard)-vulnerability assessment of hazard-affected body (vulnerability curve)-risk assessment (risk),’ importing crop model EPIC (Erosion-Productivity Impact Calculator), using crop model simulation and digital mapping techniques, quantitative assessment of spatio-temporal distribution of maize drought in China was done. The results showed that: in terms of 2, 5, 10, and 20 year return periods, the overall maize drought risk decreased gradually from northwest to southeast in the maize planting areas. With the 20 year return period, high risk value regions (drought loss rate ≥0.5) concentrate in the irrigated maize region of Northwest china, ecotone between agriculture and animal husbandry in Northern China, Hetao Irrigation Area, and north-central area of North China Plain, accounting for 6.41% of the total maize area. These results can provide a scientific basis for the government's decision-making in risk management and drought disaster prevention in China.

Simulated impacts of three decadal climate variability phenomena on dryland corn and wheat yields in the Missouri River Basin

The Missouri River Basin (MRB) is the largest river basin in the U.S. and is one of the most important crop and livestock-producing regions in the world. In a previous study of associations between decadal climate variability (DCV) phenomena and hydro-meteorological (HM) variability in the MRB for the March–August period, and their impacts on stream flow in the MRB, it was found that positive and negative phases of the Pacific Decadal Oscillation (PDO), the tropical Atlantic sea-surface temperature gradient variability (TAG), and the west Pacific Warm Pool (WPWP) temperature variability were significantly associated with decadal variability in precipitation and surface air temperature in the MRB, with combinations of various phases of these DCV phenomena associated with dry, wet, or neutral HM conditions. It was also found that these DCV phenomena impact stream flow in the MRB substantially via their association with MRB hydro-meteorology. In the present study, the Erosion Productivity Impact Calculator model, also known as the Environmental Policy Integrated Climate (EPIC) model, calibrated and validated for the MRB, was used to simulate yields of dryland corn ( Zea mays L.) and spring and winter wheat ( Triticum aestivum L.), in response to HM anomalies associated with the three DCV phenomena. Realistic values of indices of the three DCV phenomena have major impacts on crop yields, as much as 40–50% of average yield in some locations in the MRB and also evident in MRB-aggregated crop yields; however, our results show that the impacts can be location-specific. Since each of the three DCV phenomena can persist in one phase or another for several years to a decade or longer, and since the simultaneous correlation among these phenomena is negligibly small, their combined and cumulative positive/negative effects on the MRB HM and agricultural production can be dramatic in this major American and global “bread basket”. In addition, EPIC's success in simulating long-term crop yields in the MRB, taking technology trends into account, suggests that, if the evolution of major DCV phenomena can be forecast, it may be possible to forecast, as well, some multiyear to decadal measure of crop yields in the MRB with some skill.

Assessment on Typical Drought Risk for Wheat Production in China Based on Natural Vulnerability

Food security is the base of human survival and national stability.,of which food supply-demand balance variation and its induced food price fluctuation are the two major concerns.Global food production has been kept on increase since 2000,though in a slow pace,which should be able to meet the basic diet requirement.According to the 4th IPCC report,however,from the end of 2007,the worldwide food supply problem has been paid great attention to due to a sky rocket of food price.The earth is getting warmer and the frequency of drought is increased. Consequently,the intensification of water stress to crops,resulted from both the long term climate change and increase of extreme weather events,may lead to the reduction of crop yield and the increase of drought disasters. To understand the mechanism of crop drought disaster process is the key to the understanding of food security and hence a possible solution to the problem.In the past researches,the concepts of physical vulnerability and social vulnerability are not always yet explicitly distinguished or separated regarding to agricultural drought disaster risk assessment,which restricts the understanding of process-based mechanism of disaster risk dramatically.The major focuses of studies on drought disaster risk assessment range from statistical analysis and probability analysis to prediction of drought hazard or disaster losses,whilst few attached importance to the quantitative separation of physical and social vulnerabilities. The goals of this paper are to explore and quantify the physical vulnerability and drought hazard and risk.The detailed process is as follows:Firstly,agricultural drought disaster risk assessment model is developed based on the physical vulnerability of crops.This model covers agricultural drought hazard possibility distribution estimation, physical vulnerability curve estimation,land use-based exposure assessment,coping capacity integration and risk assessment.Secondly,a case study on two typical wheat species in China is carried out,and a quantitative physical vulnerability to agricultural drought hazards is analyzed using a physical-process based crop growth model and Erosion Productivity Impact Calculator(EPIC).At the beginning,the one-dimension(point scale) crop growth model EPIC with a capability of depicting water stress and temperature stress is extended to a two-dimension(area scale) model,and the Spatial Erosion Productivity Impact Calculator(S-EPIC) can be used to simulate crop yield under different intensity scenarios of drought.Two extreme scenarios for crop growth and yield simulation were investigated. Finally,the maps of drought risk occurring every 2,5,10 and 20 years were charted separately based on the calculated probable distribution of drought hazard and the vulnerability curve.The results reveal that the wheat yield loss caused by drought is decreased from the northwestern part to the southeastern part of China.