Stephen D. DeGloria

Effect of grid size on runoff and soil moisture for a variable‐source‐area hydrology model

Soil chemical and biological dynamics in mixed use landscapes are dependent on the distribution and pattern of soil moisture and water transport. In this paper we examine the effect of different grid sizes on soil water content for a spatially explicit, variable-source-area hydrology model applied to a watershed in central New York. Data on topography, soil type, and land use were input at grid sizes from 10 to 600 m. Output data consisted of runoff and spatial pattern of soil moisture. To characterize the spatial variability at different grid sizes, information theory was used to calculate the information content of the input and output variables. Simulation results showed higher average soil water contents and higher evaporation rates for large grid sizes. During a wet year, runoff was not affected by grid size, whereas during a dry year runoff was greatest for the smallest grid size. While the information content (i.e., spatial variability) of soil type and land use maps was not affected by the different grid sizes, increasing grid sizes caused the information content of the slope gradient to decrease slightly and the Laplacian (or curvature of the landscape) to decrease greatly. In other words, increasing grid cell size misrepresented the curvature of the landscape. During wet periods the decrease in information content of the soil moisture data was the same as for the Laplacian as grid size increased. During dry periods, when local fluxes such as evaporation and runoff determine the moisture content, this relation did not exist. The Laplacian can be used to provide a priori estimates of the moisture content deviations by aggregation. These deviations will be much smaller for the slowly undulating landscapes than the landscape with steep valleys simulated in this study.

Regional water flow and pesticide leaching using simulations with spatially distributed data

Abstract Simulation modeling of water and chemical movement and techniques of Geographical Information Systems (GIS) were used to integrate, condense and summarize the large-scale (order of kilometers) behavior of spatially variable soils to provide management guidance on issues related to pesticide movement in soil. A one-dimensional, convection-dispersion-based solute transport model was used to simulate the movement of four classes of chemicals through layered soils for a 7 km X 10 km site near Albany, New York. The site was divided into a 163 X 112 grid (0.4 ha per cell), with multiple model executions used as a means of incorporating the influence of spatial variability of soil hydraulic properties into the study. Pedotransfer functions based on regression equations were used to relate soil physical properties to the mean and variance of hydraulic properties within each cell. Each model execution used hydraulic properties selected from frequency distributions derived from these parameters. Land use, soil, and slope maps were overlain with a Geographical Information System (GIS) to stratify the grid cells appropriate for modeling. Time series plots for mean and variance of water flux, solute mass, and solute flux were produced based on 25 1-year simulations for each hydraulic group, using local climatic data. Two climatic conditions were analyzed, a very ‘wet’ year (90th percentile of the population of total annual precipitation) and a mean precipitation year. The calculated solute fluxes are discussed in terms of the implications for pesticide management and for estimating potential leaching hazards at the study site.

Gaussian Maximum Likelihood and Contextual Classification Algorithms for Multicrop Classification

In this paper we review some of the ways in which context has been handled in the remote-sensing literature, and we introduce additional possibilities. The problem of computing exhaustive and normalized class-membership probabilities from the likelihoods provided by the Gaussian maximum likelihood classifier (to be used as initial probability estimates to start relaxation) is discussed. An efficient implementation of probabilistic relaxation is proposed, suiting the needs of actual remote-sensing applications. A modified fuzzy-relaxation algorithm using generalized operations between fuzzy sets is presented. Combined use of the two relaxation algorithms is proposed to exploit context in multispectral classification of remotely sensed data. Results on both one artificially created image and one MSS data set are reported.

Land use dynamics within an urbanizing non-metropolitan county in New York State (USA)

Land use/land cover data for fifteen minor civil divisions (MCDs) in Ulster County, New York (USA) were interpreted from 1968 and 1985 aerial photographs. These data were combined with ancillary physiographic and demographic data as raster layers within a computerized geographic information system (GIS). Class to class changes in land use/land cover were quantified for a study area approximately 30 kilometers by 50 kilometers. The relationships between the land use/land cover variables and the ancillary variables were modeled in a series of weighted least squares regressions employing data spatially aggregated by general soil map unit (N = 44).Between 1968 and 1985, nearly one-third of the study area changed to another land use/land cover class. Land in the urban class increased from 6.7% to 17.8% of the study area, while the forest class declined from 65.0% to 55.2%, and the agriculture class declined from 12.7% to 8.9%. Gains and losses in the remaining five major (Level I) land use/land cover classes were relatively small. Land use changes primarily involved the conversion of land from the forest, agriculture, and vacant classes to the urban class, and from the agriculture class to the forest and vacant classes. Variables accounting for the variance in the land use/land cover class proportions of the soil units were population density, highway proximity, distance to urban centers, mean elevation, mean slope gradient, and soil suitability for farming and for urban development.

Error assessment in decision-tree models applied to vegetation analysis

Methods were developed to evaluate the performance of a decision-tree model used to predict landscape-level patterns of potential forest vegetation in central New York State. The model integrated environmental databases and knowledge on distribution of vegetation. Soil and terrain decision-tree variables were derived by processing state-wide soil geographic databases and digital terrain data. Variables used as model inputs were soil parent material, soil drainage, soil acidity, slope position, slope gradient, and slope azimuth. Landscapescale maps of potential vegetation were derived through sequential map overlay operations using a geographic information system (GIS). A verification sample of 276 field plots was analyzed to determine: (1) agreement between GIS-derived estimates of decision-tree variables and direct field measurements, (2) agreement between vegetation distributions predicted using GIS-derived estimates and using field observations, (3) effect of misclassification costs on prediction agreement, (4) influence of particular environmental variables on model predictions, and (5) misclassification rates of the decision-tree model. Results indicate that the prediction model was most sensitive to drainage and slope gradient, and that the imprecision of the input data led to a high frequency of incorrect predictions of vegetation. However, in many cases of misclassification the predicted vegetation was similar to that of the field plots so that the cost of errors was less than expected from the misclassification rate alone. Moreover, since common vegetation types were more accurately predicted than rare types, the model appears to be reasonably good at predicting vegetation for a randomly selected plot in the landscape. The error assessment methodology developed for this study provides a useful approach for determining the accuracy and sensitivity of landscape-scale environmental models, and indicates the need to develop appropriate field sampling procedures for verifying the predictions of such models.

EFFECT ON SOIL PROPERTIES OF CONVERSION OF YELLOW RIVER DELTA ECOSYSTEMS

Using remote sensing and geographic information system technologies, we analyzed changes in ecosystem boundary conditions in the Yellow River Delta. We investigated variations in soil water, bulk density, total nitrogen, total phosphorus, and organic matter, as well as concentrations of soluble Ca2+, K+, Mg2+ and Na+, under different ecosystem conversions. Results indicated that from 1992 to 2006, boundary characteristics became more complicated and ecosystem conversion was mainly from farmland to a mixed ecosystem supportingTamarix chinensis-Phragmites communis. These ecosystem conversions may be attributed to a combination of urban expansion, oil exploration and extraction, water interception, and soil salinization. Ecosystem conversion also affected soil properties. Organic matter differed among the ecosystems, as did the concentrations of the soil base cations. Ca2+ concentration was higher than concentrations of other cations, and significant differences existed in Ca2+ and Mg2+ concentrations among ecosystems. While the concentration of K+ and Mg2+ showed similar concentrations, mostly increasing, among different ecosystem conversions, Na+ concentrations decreased. In summary, the concentrations of soluble minerals were significantly influenced by ecosystem conversions.

Estimating Soil Organic Carbon Stocks and Spatial Patterns with Statistical and GIS-Based Methods

Accurately quantifying soil organic carbon (SOC) is considered fundamental to studying soil quality, modeling the global carbon cycle, and assessing global climate change. This study evaluated the uncertainties caused by up-scaling of soil properties from the county scale to the provincial scale and from lower-level classification of Soil Species to Soil Group, using four methods: the mean, median, Soil Profile Statistics (SPS), and pedological professional knowledge based (PKB) methods. For the SPS method, SOC stock is calculated at the county scale by multiplying the mean SOC density value of each soil type in a county by its corresponding area. For the mean or median method, SOC density value of each soil type is calculated using provincial arithmetic mean or median. For the PKB method, SOC density value of each soil type is calculated at the county scale considering soil parent materials and spatial locations of all soil profiles. A newly constructed 1∶50,000 soil survey geographic database of Zhejiang Province, China, was used for evaluation. Results indicated that with soil classification levels up-scaling from Soil Species to Soil Group, the variation of estimated SOC stocks among different soil classification levels was obviously lower than that among different methods. The difference in the estimated SOC stocks among the four methods was lowest at the Soil Species level. The differences in SOC stocks among the mean, median, and PKB methods for different Soil Groups resulted from the differences in the procedure of aggregating soil profile properties to represent the attributes of one soil type. Compared with the other three estimation methods (i.e., the SPS, mean and median methods), the PKB method holds significant promise for characterizing spatial differences in SOC distribution because spatial locations of all soil profiles are considered during the aggregation procedure.

Improving risk estimates of runoff producing areas: formulating variable source areas as a bivariate process.

Predicting runoff producing areas and their corresponding risks of generating storm runoff is important for developing watershed management strategies to mitigate non-point source pollution. However, few methods for making these predictions have been proposed, especially operational approaches that would be useful in areas where variable source area (VSA) hydrology dominates storm runoff. The objective of this study is to develop a simple approach to estimate spatially-distributed risks of runoff production. By considering the development of overland flow as a bivariate process, we incorporated both rainfall and antecedent soil moisture conditions into a method for predicting VSAs based on the Natural Resource Conservation Service-Curve Number equation. We used base-flow immediately preceding storm events as an index of antecedent soil wetness status. Using nine sub-basins of the Upper Susquehanna River Basin, we demonstrated that our estimated runoff volumes and extent of VSAs agreed with observations. We further demonstrated a method for mapping these areas in a Geographic Information System using a Soil Topographic Index. The proposed methodology provides a new tool for watershed planners for quantifying runoff risks across watersheds, which can be used to target water quality protection strategies.

Potential for arsenic contamination of rice in Bangladesh: spatial analysis and mapping of high risk areas

Knowledge of the location and severity of arsenic contamination in Bangladesh is required to develop land and resource management strategies to reduce human exposure to arsenic and arsenic contamination of food and water supplies. Potential high risk areas for arsenic contamination of rice were identified using spatial analysis and modelling. Existing country-wide data on groundwater arsenic contamination, winter (boro) rice production and irrigation methods were used to identify areas where high production of groundwater irrigated boro rice corresponds to areas with high arsenic contamination of groundwater. Results show that 76% of irrigated boro rice is grown in upazila where mean groundwater arsenic concentrations are below 50 µg L-1, the Bangladesh health standard. Seven percent, however, is grown in areas with mean concentrations greater than 100 µg L-1, primarily in south-central and western-central Bangladesh. Mitigation strategies are suggested for the areas considered to be at risk for arsenic contamination of boro rice.

Spatiotemporal dynamics of cattle behavior and resource selection patterns on East African rangelands: evidence from GPS-tracking

ABSTRACT Characterizing cattle behavior is crucial to inferring fine-scale resource selection patterns and improving rangeland management. However, our understanding of cattle behavior and resource selection on the extensive African rangelands suffers from a lack of quantitative, continuous and inter-seasonal monitoring of cattle movement. Based on integration of GPS-tracking and field observations, this study links cattle behavioral types with statistical parameters of movement, analyzes spatiotemporal dynamics of behavior and predicts resource selection patterns in Borana Zone of southern Ethiopia. We find that different cattle behavioral types were associated with distinct ranges of movement velocity. Distribution of identified cattle behavior varied substantially within the day and along the distance gradient from camp locations. Vegetation greenness, topography, study site, herding strategy and season were dominant factors influencing foraging areas selection by cattle. Research findings suggested that extensive herding through camp relocation can promote forage uptake while reducing energy spent on traveling. Future modeling of cattle resource selection needs to be based on longer-term GPS-tracking data and incorporate additional social, environmental, institutional and cultural factors to better interpret the complexity associated with cattle behavior in extensive grazing systems.