Abstract. 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. IntroductionNonpoint source and habitat degradation problems must be addressed at the basin or watershed level for efficient management of water quality [U.S. Environmental Protection Agency, 1994Agency, , 1995Agency, , 1996 GIS-Based Hydrology ModelThe analysis was performed with a GIS-based model originally developed by Zollweg et al. [1996]

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Accurately measuring soil organic matter content (SOM) in paddy fields is important because SOM is one of the key soil properties controlling nutrient budgets in agricultural production systems. Estimation of this soil property at an acceptable level of accuracy is important; especially in the case when SOM exhibits strong spatial dependence and its measurement is a time‐ and labor‐consuming procedure. This study was conducted to evaluate and compare spatial estimation by kriging and cokriging with remotely sensed data to predict SOM using limited available data for a 367‐km2 study area in Haining City, Zhejiang Province, China. Measured SOM ranged from 5.7 to 40.4 g kg−1, with a mean of 19.5 g kg−1 Correlation analysis between the SOM content of 131 soil samples and the corresponding digital number (DN) of six bands (Band 1–5 and Band 7) of Landsat Enhanced Thematic Mapper (ETM) imagery showed that correlation between SOM and DN of Band 1 was the highest (r = −0.587). We used the DN of Band 1 as auxiliary data for the SOM prediction, and used descriptive statistics and the kriging standard deviation (STD) to compare the reliabilities of the predictions. We also used cross‐validation to validate the SOM prediction. Results indicate that cokriging with remotely sensed data was superior to kriging in the case of limited available data and the moderately strong linear relationship between remotely sensed data and SOM content. Remotely sensed data such as Landsat ETM imagery have the potential as useful auxiliary variables for improving the precision and reliability of SOM prediction.

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