We all want to know the future, but what is the best way to predict what will happen? Assuming we don’t have a crystal ball or a time machine, we have to find patterns in the available information and use that make our best, informed guess. This is what scientists do. There is a spectrum … Continue reading Oracles and Science: The Trouble with Predictions
Soil mapping, classification, and pedologic modelling have been important drivers in the advancement of our understanding of soil. Advancement in one of these highly interrelated areas tend to lead to corresponding advances in the others. Traditionally, soil maps have been desirable for purposes of land valuation, agronomic planning, and even in military operations. The expansion of the use of soil knowledge to address issues beyond agronomic production, such as land use planning, environmental concerns, energy security, water security, and human health, to name a few, requires new ways to communicate what we know about the soils we map as well as bringing forth research questions that were not widely considered in earlier soils studies.
A comparison of direct and indirect approaches for mapping SOC stocks from rule-based, multiple linear regression models applied at the landscape scale via spatial association. The final products for both strategies are high-resolution maps of SOC stocks (kg m‾²), covering an area of 122 km², with accompanying maps of estimated error. Although the indirect approach fit the spatial variation better and had a lower mean estimated error for the topsoil stock, the mean estimated error for the total SOC stock (topsoil + subsoil) was lower for the direct approach. The optimal approach would depend upon the intended use of the map.
From as early as 500 BCE, humans have recognized that some things vary together in space. This is essentially correlation, but the spatial aspect sometimes adds a special twist. Also, correlation requires evaluation of quantitative data, while this concept is not limited to quantitative characteristics. For example, Diophanes of Bithynia observed that “you can judge … Continue reading CLORPT: Spatial Association in Soil Geography
Quantifying uncertainty can be a very useful and often important aspect of evaluating results of calculations, particularly in modelling. The same applies for spatial layer mashups where the grids provide the input variables for equations that are calculated spatially (i.e. raster calculator). This toolbox for ArcGIS uses standard error propagation equations to simultaneously calculate the … Continue reading Error Propagation Toolbox
Results suggest that models with limited predictor pools can substitute other predictors to compensate for unavailable variables. However, a better performing model was always found by considering predictor variables at multiple scales. Although the scale effect of the modifiable area unit problem is generally well known, this study suggests digital soil mapping efforts would be enhanced by the greater consideration of predictor variables at multiple analysis scales.
In the process of creating a map, geographers often have to engage in the activity of spatial prediction. Although there are many tools we use to accomplish this task, they generally boil down to the use of one or two fundamental concepts. Waldo Tobler is credited for identifying the ‘first law of geography’, stating “Everything … Continue reading Fundamentals of Spatial Prediction
Nitrate concentration and stream discharge data from USGS National Stream Quality Accounting Network monitoring stations in the upper Mississippi River (UMR) and Ohio River basins were used to calculate stream nitrate loading and annual flow-weighted average (FWA) nitrate concentrations. The model accounts for 90% of the variation among stations in long term FWA nitrate concentrations and was used to estimate FWA nitrate concentrations for a 100 ha grid across the UMR and Ohio River basins. To estimate potential nitrate removal by wetlands across the same grid area, mass balance simulations were used to estimate percent nitrate reduction for hypothetical wetland sites distributed across the UMR and Ohio River basins. Modeling results suggest that a 30% reduction in nitrate load from the UMR and Ohio River basins could be achieved using 210,000-450,000 ha of wetlands targeted on the highest nitrate contributing areas.