Runoff mapping using WEPP erosion model and GIS tools

Soil erosion, associated with environmental impacts and crop productivity loss, is usually considered the most impacting of surface hydrology processes. Runoff plays a major role in the erosion process, but it is also important by itself as it directly influences several surface hydrologic processes. In this paper, a computer interface (Erosion Database Interface, EDI) is described that allows processing the surface hydrology output database of the Water Erosion Prediction Project (WEPP) erosion prediction model, resulting in a georeferenced estimation of runoff. WEPP output contains non-georeferenced daily information about estimated runoff at the lower end of each Overland Flow Element. EDI, when running with WEPP, allows extracting WEPP-calculated runoff values, transforming them into annual means and relocating them in a georeferenced database readable by Geographic Information Systems (GIS). EDI was applied to a 1990 ha watershed in southeast Brazil, with vegetation of mainly sugarcane, forest, and pasture. A 100-year climate simulation was used as input to WEPP, and erosion values were calculated at about six points per hectare and interpolated to a raster format. EDI was successful in preparing the database for automatic calculation of erosion and hydrologic parameters with WEPP and to restore georeferences to mean annual accumulated runoff data that were imported in the GIS as a vector database. Of all the resulting maps, the runoff map is the one that integrates all of the input parameters required for WEPP simulation, thus reflecting not only the physical environment but also crop growth and management and tillage operations. A very small correlation between runoff and erosion shows them to behave independently. Moreover, it is concluded that on analyzing runoff related to agricultural management, georeferenced runoff studies are especially important. In this context, EDI may be a useful tool to assess the effect of tillage and crop management on runoff production.     

Assessment of erosion hotspots in a watershed: Integrating the WEPP model and GIS in a case study in the Peruvian Andes

This paper presents a case study in assessment of erosion hotspots in an Andean watershed. To do this, we made use of an interface called Geospatial Modelling of Soil Erosion (GEMSE): a tool that integrates Geographical Information Systems (GIS) with the Water Erosion Prediction Project (WEPP) model. Its advantages are: (i) it is independent of any special GIS software used to create maps and to visualize the results; (ii) the results can be used to produce response surfaces relating outputs (e.g. soil loss, runoff) with simple inputs (e.g. climate, soils, topography); (iii) the scale, resolution and area covered by the different layers can be different among them, which facilitates the use of different sources of information. The objective of this paper is to show GEMSE's performance in a specific case study of soil erosion in La Encañada watershed (Peru) where the hillslope version of WEPP has been previously validated. Resulting runoff and soil loss maps show the spatial distribution of these processes. Though these maps do not give the total runoff and soil loss at the watershed level, they can be used to identify hotspots that will aid decision makers to make recommendations and plan actions for soil and water conservation.     

Evaluation of two empirical wind erosion models in arid and semi-arid regions of China and the USA

Wind erosion models are important tools for assessing soil erodibility and identifying management practices to control erosion. The Agricultural Policy/Environmental eXtender (APEX) model and Revised Wind Erosion Equation (RWEQ) were tested using data collected from the Tarim Basin of China and Columbia Plateau of the United States of America. Adequate performance in simulating soil loss was achieved using the original APEX model and RWEQ in respectively a cotton field and desert-oasis ecotone in the Tarim Basin and winter wheat - summer fallow (WW-SF) field in the Columbia Plateau. We calibrated the APEX model and RWEQ to improve performance because both models have many empirical parameters. After calibration, both models adequately simulated soil loss from all land use types except the RWEQ from the red date orchard in the Tarim Basin. Inadequate performance of the calibrated RWEQ in the red date orchard was likely due to underestimating maximum mass transport.     

Improved classification of conservation tillage adoption using high temporal and synthetic satellite imagery

Conservation tillage management has been advocated for carbon sequestration and soil quality preservation purposes. Past satellite image analyses have had difficulty in differentiating between no-till (NT) and minimal tillage (MT) conservation classes due to similarities in surface residues, and may have been restricted by the availability of cloud-free satellite imagery. This study hypothesized that the inclusion of high temporal data into the classification process would increase conservation tillage accuracy due to the added likelihood of capturing spectral changes in MT fields following a tillage disturbance. Classification accuracies were evaluated for Random Forest models based on 250-m and 500-m MODIS, 30-m Landsat, and 30-m synthetic reflectance values. Synthetic (30-m) data derived from the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) were evaluated because high frequency Landsat image sets are often unavailable within a cropping season due to cloud issues. Classification results from a five-date Landsat model were substantially better than those reported by previous classification tillage studies, with 94% total and ≥ 88% class producer's accuracies. Landsat-derived models based on individual image scenes (May through August) yielded poor MT classifications, but a monthly increase in accuracy illustrated the importance of temporal sampling for capturing regional tillage disturbance signatures. MODIS-based model accuracies (90% total; ≥ 82% class) were lower than in the five-date Landsat model, but were higher than previous image-based and survey-based tillage classification results. Almost all the STARFM prediction-based models had classification accuracies higher than, or comparable to, the MODIS-based results (> 90% total; ≥ 84% class) but the resulting model accuracies were dependent on the MODIS/Landsat base pairs used to generate the STARFM predictions. Also evident within the STARFM prediction-based models was the ability for high frequency data series to compensate for degraded synthetic spectral values when classifying field-based tillage. The decision to use MODIS or STARFM-based data within conservation tillage analysis is likely situation dependent. A MODIS-based approach requires little data processing and could be more efficient for large-area mapping; however a STARFM-based analysis might be more appropriate in mixed-pixel situations that could potentially compromise classification accuracy.     

Assessing crop residue cover using shortwave infrared reflectance

Management of crop residues is an important consideration for reducing soil erosion and increasing soil organic carbon. Current methods of measuring residue cover are inadequate for characterizing the spatial variability of residue cover over large fields. The objectives of this research were to determine the spectral reflectance of crop residues and soils and to assess the limits of discrimination that can be expected in mixed scenes. Spectral reflectances of dry and wet crop residues plus three diverse soils were measured over the 400-2400 nm wavelength region. Reflectance values for scenes with varying proportions of crop residues and soils were simulated. Additional spectra of scenes with mixtures of crop residues, green vegetation, and soil were also acquired in corn, soybean, and wheat fields with different tillage treatments. The spectra of dry crop residues displayed a broad absorption feature near 2100 nm, associated with cellulose-lignin, that was absent in spectra of soils. Crop residue cover was linearly related (r²=0.89) to the Cellulose Absorption Index (CAI), which was defined as the relative depth of this absorption feature. Green vegetation cover in the scene attenuated CAI, but was linearly related to the Normalized Difference Vegetation Index (NDVI, r²=0.93). A novel method is proposed to assess soil tillage intensity classes using CAI and NDVI. Regional surveys of soil conservation practices that affect soil carbon dynamics may be feasible using advanced multispectral or hyperspectral imaging systems.     

Maximizing the first eigenfrequency of structures subjected to uniform boundary erosion through the level set method

Engineering with Computers - Erosion of structure may result in non-trivial loss of performance. For instance, erosion may make structure members break apart and results in localized vibration...     

Mitigating the effects of soil and residue water contents on remotely sensed estimates of crop residue cover

Crop residues on the soil surface decrease soil erosion and increase soil organic carbon and the management of crop residues is an integral part of many conservation tillage systems. Current methods of measuring residue cover are inadequate for characterizing the spatial variability of residue cover over large fields. The objectives of this research were to determine the effects of water content on the remotely sensed estimates of crop residue cover and to propose a method to mitigate the effects of water content on remotely sensed estimates of crop residue cover. Reflectance spectra of crop residues and soils were measured in the lab over the 400-2400 nm wavelength region. Reflectance of scenes with various residue cover fractions and water contents was simulated using a linear mixture model. Additional spectra of scenes with mixtures of crop residues and soil were also acquired in corn, soybean, and wheat fields with different tillage treatments and different water content conditions. Crop residue cover was linearly related to the cellulose absorption index (CAI), which was defined as the relative intensity of an absorption feature near 2100 nm. Water in the crop residue significantly attenuated CAI and changed the slope of the residue cover vs. CAI relationship. Without an appropriate correction, crop residue covers were underestimated as scene water content increased. Spectral vegetation water indices were poorly related to changes in the water contents of crop residues and soils. A new reflectance ratio water index that used the two bands located on the shoulders of the cellulose absorption feature to estimate scene water conditions was proposed and tested with data from corn, soybean, and wheat fields. The ratio water index was used to describe the changes in the slope of crop residue cover vs. CAI and improve the predictions of crop residue cover. These results indicate that spatial and temporal adjustments in the spectral estimates of crop residue cover are possible. Current mutispectral imaging systems will not provide reliable estimates of crop residue cover when scene water content varies. Hyperspectral data are not required, because the three narrow bands that are used for both CAI and the scene moisture correction could be incorporated in advanced multispectral sensors. Thus, regional surveys of soil conservation practices that affect soil carbon dynamics may be feasible using either advanced multispectral or hyperspectral imaging systems.     

免耕措施对土壤水分及利用效率的影响

免耕覆盖能有效地控制土壤侵蚀,抑制地表起沙,防治土壤退化,将是我国北方地区代替传统农耕的最好办法之一。针对土壤水分往往是我国北方地区农作物生长重要的限制性因子,本文选择国内外免耕措施的土壤水分试验研究进行总结,系统地分析了免耕与传统农耕的土壤水分动态变化特征,评价了免耕土壤水分利用效率。结果表明,与传统耕作相比,免耕普遍可增加土壤水2%~8%,但只有长期实施免耕和覆盖达到一定程度时,免耕的增水效果才明显,而免耕水分利用效率却因产量、降水等而不同。     

A GIS-based modeling approach for implementation of sustainable farm management practices

Few agricultural producers utilize the true analytical power of GIS and computer simulation models, partly because the loose linkages developed to-date between GIS and most public-domain modeling software are extremely cumbersome to use. The integrated system (EPIC–View) developed in the study allows the integration of a comprehensive hydrologic–crop management model (EPIC) with a desktop GIS to function as a planning tool aimed at implementing sustainable farm management practices. The use of GIS makes possible the integration of diverse spatial data into a comprehensive spatial database. EPIC–View is applied to simulate nitrogen (N) dynamics under conventional and minimum tillage conditions of a field located in Caddo County, Oklahoma. In general, the overall N balance obtained under minimum tillage is better than the balance obtained under conventional tillage over a 5-year model run. Unexplained losses of N averaged 9.55% and 4.2% of the gain in N under conventional and minimum tillage respectively. The integrated modeling system holds immense potential as a farm management tool. Various components of a sustainable agricultural system including irrigation management, crop management, soil management, and pest management, can be efficiently managed. This approach could make farms more economically viable and ecologically sound.     

Nonlinear hierarchical models for predicting cover crop biomass using Normalized Difference Vegetation Index

Incorporating cover crops into agricultural systems can improve soil structural properties, increase nutrient availability, reduce erosion and loss of agrochemicals, and suppress weeds. These benefits are a function of the amount of cover crop biomass that enters the soil. The ability to easily and inexpensively quantify the spatial variability of cover crop biomass is needed to better understand and predict its potential as an input to agricultural systems. Here, we explore the use of Normalized Difference Vegetation Index (NDVI) as a source of information for improving accuracy and precision of cover crop biomass prediction. We focus on developing models that account for biomass variability within and among fields. These models are used to produce digital data layers of predicted biomass and associated uncertainty. We propose hierarchical nonlinear models with field random effects and a residual variance function to accommodate strong heteroscedasticity. These models are motivated using aboveground biomass of red clover (Trifolium pratense L.) measured on three different dates in five fields in southwest Michigan. Model adequacy was assessed using the Deviance Information Criterion. Given this criterion, the “best” fitting model included field effects and a polynomial function to account for non-constant residual variance. Importantly, we demonstrate that accounting for heteroscedasticity in the model fitting is critical for capturing uncertainty in subsequent biomass prediction.     

Assimilating satellite imagery and visible–near infrared spectroscopy to model and map soil loss by water erosion in Australia

Soil loss causes environmental degradation and reduces agricultural productivity over large areas of the world. Here, we use the latest earth observation data and soil visible–near infrared (vis–NIR) spectroscopy to estimate the factors of the Revised Universal Soil Loss Equation (RUSLE) and to model soil loss by water erosion in Australia. We estimate rainfall erosivity (R) using the Tropical Rainfall Measuring Mission (TRMM); slope length and steepness (L and S) using a 3-arcsec Shuttle Radar Topography Mission (SRTM) digital elevation model; cover management (C) and control practice (P) using the national dynamic land cover dataset (DLCD) of Australia derived from the moderate-resolution imaging spectroradiometer (MODIS); and soil erodibility (K) using vis–NIR estimates of the contents of sand, silt, clay and organic carbon in Australian soil. We model K using a machine-learning algorithm with environmental predictors selected to best capture the factors that influence erodibility and produced a digital map of K. We use the derived RUSLE factors to estimate soil loss at 1-km resolution across the whole of Australia. We found that the potential gross average soil loss by water erosion in Australian is 1.86 t ha⁻¹ y⁻¹ (95% confidence intervals of 1.78 and 1.93 t ha⁻¹ y⁻¹), equivalent to a total of 1242 × 10⁶ tonnes of soil lost annually (95% confidence intervals of 1195 and 1293 t × 10⁶ y⁻¹). Our estimates of erosion are generally smaller than previous continental estimates using the RUSLE, but particularly in croplands, which might indicate that soil conservation practices effectively reduced erosion in Australia. However we also identify localized regions with large erosion in northern Australia and northeastern Queensland. Erosion in these areas carries sediments laden with nitrogen, phosphorus and pollutants from agricultural production into the sea, negatively affecting marine ecosystems. We used the best available data and our results provide better estimates compared to previous assessments. Our approach will be valuable for other large, sparsely sampled areas of the world where assessments of soil erosion are needed.     

Modelling and testing spatially distributed sediment budgets to relate erosion processes to sediment yields

Identifying the erosion processes contributing to increased basin fine sediment yield is important for reducing downstream impacts on aquatic ecosystems. However, erosion rates are spatially variable, and much eroded sediment is stored within river basins and not delivered downstream. A spatially distributed sediment budget model is described that assesses the primary sources (hillslope soil erosion, gully and riverbank erosion) and sinks (floodplain and reservoir deposition) of fine sediment for each link in a river network. The model performance is evaluated in a 17,000-km² basin in south-east Australia using measured suspended sediment yields from eight catchments within the basin, each 100–700 km² in area. Spatial variations within the basin in yield and area-specific yield were reliably predicted. Observed yields and area-specific yields varied by 17-fold and 15-fold respectively between the catchments, while predictions were generally within a factor of 2 of observations. Model efficiency at predicting variations in area-specific yield was good outside forested areas (0.58), and performance was weakly sensitive to parameter values. Yields from forested areas were under-predicted, and reducing the predicted influence of riparian vegetation on bank erosion improved model performance in those areas. The model provided more accurate and higher resolution predictions than catchment area interpolation of measured yields from neighbouring river basins. The model is suitable for guiding the targeting of remediation measures within river basins to reduce downstream sediment yields.     

民勤县红崖山风蚀监测站监测成果分析

民勤县红崖山风蚀监测站是甘肃省2个风蚀监测点之一,为准确掌握区域水土流失强度、发生发展规律、动态变化趋势等情况,以现有监测技术为基础,利用全自动气象观测仪、测钎、布袋式集沙仪、降尘缸等设备,通过地面观测法、测钎法、降尘缸法等,对监测站的降水量、蒸发量、风速、风向、土壤含水率、风蚀厚度、降尘量、输沙率等要素数据进行定期监测,分析得出：每年第2季度风蚀厚度最大,这与大风天气发生频次、风速大小有直接关系。采取麦草方格网沙障措施可有效降低风蚀厚度,水土流失治理效果明显。通过对该监测站的风蚀情况进行监测分析,为今后民勤县开展水土流失防治工作提供重要的基础支撑。     

Optimal classification methods for mapping agricultural tillage practices

The classification of agricultural tillage systems has proven challenging in the past using traditional classification methods due to the similarity of spectral reflectance signatures of soils and senescent crop residues. In this study, five classification methods were examined to determine the most suitable classification algorithm for the identification of no-till (NT) and traditional tillage (TT) cropping methods: minimum distance (MD), Mahalanobis distance, Maximum Likelihood (ML), spectral angle mapping (SAM), and the cosine of the angle concept (CAC). A Landsat ETM+ image acquired over southern Michigan and northern Indiana was used to test these classification methods. Each classification method was validated with 293 ground truth sampling locations collected commensurate with the satellite overpass. Classification accuracy was then assessed using error matrix analysis, Kappa statistics, and tests for statistical significance. The results indicate that of the classification routines examined, the two spectral angle methods were superior to the others. The cosine of the angle concept algorithm outperformed all the other classification routines for tillage practice identification and mapping, yielding an overall accuracy of 97.2% (Kappa=0.959).     

基于形态学的十字目标定位方法研究

为了提高微操作系统的装配精度,提出了一种基于形态学腐蚀算法和Hough变换的十字目标亚像素中心定位方法。首先通过选取适当的结构元素,分别对组成十字图像的垂直、水平方向的直线段进行腐蚀处理,得到含有十字中心信息的水平和垂直的两条行、列像素,行、列的相交点即为十字图像特征的中点;然后选取适当参数空间,对行、列进行Hough变换并将结果记入参数空间累加器,最后对区域内点进行加权平均处理,得到十字图像亚像素中心定位。实验结果表明：该方法具有定位速度快,定位精度高的优点。     

Comparison between calculated and measured thermodynamic data of liquid (Ag, Au, Cu)-Sn-Zn alloys

The activity of zinc was measured in a series of liquid alloys in the three ternary systems, Ag-Sn-Zn [94Kar], Au-Sn-Zn [96Kar] and Cu-Sn-Zn [97Pen], with an emf (electromotive for e) method. From these measurements and the slope of the emf vs. temperature (dE/dT) all thermodynamic properties were derived. The experimental values were compared with calculated results. For these calculations four different models were applied and the thermodynamic data of the binary systems, obtained from the literature, were used.     

Modeling of Cd and Pb speciation in soil solutions by WinHumicV and NICA-Donnan model

Speciation modeling is an essential tool to estimate the chemical composition of an aqueous sample and to assess the bioavailability and potential risks associated with metal contamination. It is also an indispensable alternative for quantifying the metal speciation when analytical techniques are not available. In this study, we tested the effectiveness of two models, WinHumicV and the Non-Ideal Competitive Adsorption (NICA)-Donnan model, in predicting the amount of Cd²⁺ and Pb²⁺, which were originally measured by anodic stripping voltammetry (ASV). In general, with its default parameters, WinHumicV satisfactorily estimated the free Cd concentrations. For Pb, at high pH the model predictions were lower than the ASV measurements. The discrepancy between the measured and predicted free Pb concentrations may be attributed to the uncertainty of stability constants in WHAM model V for metal–DOC interactions, or the possible overestimation of free metals by ASV at high pH. Using the published generic parameters for fulvic acid (FA), the consistent NICA-Donnan model also made predictions comparable to the experimental data when Al and Fe competition was taken into account in the model. The agreement of model predictions and experimental data demonstrates that both models provide reasonable predictions of the metal speciation in soil solutions. However, the results stress the requirement of estimates or measurements of Al and Fe concentrations in soil solution speciation to provide reasonable estimates of trace element speciation.     

Prediction of soil erodibility factor for Peninsular Malaysia soil series using ANN

Soil erodibility factor (susceptibility of soil to be lost to erosion) is one of the components of the universal soil loss equation. This study presents an artificial neural network (ANN) model using 74 soil series provided by the Department of Agriculture, Malaysia. The ANN model produces acceptable results: the K values for 74 soil series of Peninsular Malaysia give much better information to engineers in determining the soil loss and sediment yield for a given development area.     

Impact of climate change on sea level rise in Lagos,Nigeria

Sea level rise and ocean surge are the major potential impacts of climate change in the rapidly growing urban Lagos in Nigeria. Coastal inundation is, however, expected to increase problems of flooding and intrusion of sea water into freshwater sources and ecosystems, thereby heightening the social conflict already prevalent in this area. This article examines the historical trend in the coastal extent of one of the most populous coastal cities in the tropics and projects the potential impact on coastline change using geographic information system (GIS) techniques coupled with scenario-based climate change predictions from three different general circulation models (GCMs). This study aims, therefore, to provide empirical reasoning for the development of sea defence policies which would help in the reduction of possible loss of life or capital asset damage by suggesting adequate and cost-effective flood warning systems as well as by discouraging inappropriate infrastructural development in areas at risk from flooding and coastal erosion.     

Terrace erosion and sediment transport model: a new tool for soil conservation planning in bench-terraced steeplands

Despite widespread bench-terracing soil erosion remains a major problem in Java’s uplands. To elucidate the causes for this lack of impact, runoff and erosion processes were studied at a variety of spatial scales within a volcanic catchment in West Java. Research indicated that soil loss occurs via rain splash and wash of rainfall-detached sediment by shallow overland flow from the terrace riser and bed, and via runoff entrainment of sediment deposited in the central terrace toe drain. The terrace erosion and sediment transport mode (TEST) was developed to physically describe these processes, as a function of vegetation and soil surface cover where appropriate, yet use as few parameters as possible. Runoff generation was described by the spatially variable infiltration model (SVIM) and a two parameter rainfall depth-intensity distribution was assumed to derive a simple analytical expression for storm runoff depth. In a similar manner expressions were derived for effective rainfall kinetic energy to predict rainfall-driven transport using a newly developed model, and for effective runoff rate to predict flow-driven transport using GUEST model theory. The model and its components were tested using measured runoff and soil loss from 31 sections of terrace beds or risers and from six terrace units during two seasons. The model generally performed satisfactorily and provides a useful new tool for assessing the impacts of soil conservation measures on bench terrace runoff and soil loss.