An examination of geographic patterns of soil climate and its classification in the U.S. system of soil taxonomy
Soil climate, the record of temporal patterns of soil moisture and temperature, is an important component of the structure of U.S. Soil Taxonomy. The U.S. Soil Survey has used the Newhall Simulation Model (NSM) for estimating soil climate from atmospheric climate records at weather stations since the 1970s. The current soil climate map of the U.S. was published in 1994 by using NSM runs from selected weather stations along with knowledge-based hand-drawn mapping procedures. We developed a revised soil climate mapping methodology using the NSM and digital soil mapping techniques.^ The new methodology is called Grid Element Newhall Simulation Model (GEN), where a coordinate system is used to divide geographic space into a grid and each element or grid-cell serves as a reference area for querying and organizing model input, and for organizing and displaying model output. The GEN was used to make a soil moisture map of the conterminous U.S. (GEN-CONUS). GEN-CONUS and the 1994 map were compared to each other and to two sets of weather station data from years 1961 to 1990 and years 1971 to 2000 (National climate data center, NCDC). Agreement between GEN-CONUS and the 1994 map was 75.6%. GEN-CONUS had higher agreement than the 1994 map with NSM output from NCDC data for 1961-1990 and 1971-2000 (kappa = 0.845 and 0.777). The GEN methodology was also used to generate a map of projected soil climate in the year 2080 for part of the Southern Rocky Mountains, predicting expansion of the Ustic and contraction of the Udic moisture regimes.^ Soil climate in the conterminous US is expected to change in response to global climate change. Soil moisture and temperature are strongly influenced by atmospheric climate variables. The Grid Element Newhall Simulation Model (GEN), an updated NSM for geographic raster data, was developed and applied in this project to future climate simulations available from International atmospheric climate prediction projects. These included a simulation of 1) current climate conditions, 2) climate in year 2070 under a radiative forcing increase scenario of 2.6 W m-2 above pre-industrial levels (a low estimate) and 3) climate in the year 2070 under a radiative forcing scenario increase of 8.5 W m-2 (higher estimate).^ As a driver of soil development and a key factor of soil formation, climate influences physical and chemical properties of soils as they form from geological and biological material. In this study we examine soil climate as simulated by the NSM and its relation to georeferenced point observations of soil properties measured and recorded over many decades by the National Cooperative Soil Survey. The goal is to determine the strength and direction of relationships between geographic observations of soil properties that may have been influenced by climate and the simulations of soil climate for the same locations. An additional goal is to determine whether the NSM as a process model contributes substantially to an accounting of the interaction between atmospheric climate and any resulting soil properties, or whether a simpler observational model that does not include simulation of soil moisture and temperature interactions might be sufficient or superior to this simulation approach. The observational model includes the same input directly taken from atmospheric climate datasets as that used to populate the NSM, but does not include simulation of how the atmospheric climate would translate into soil climate through simulation of moisture and temperature dynamics in the soil.^ We find that the NSM may have some value as a tool to explain a few relationships between climate and soil properties observed in the NCSS dataset, but that direct observation without simulation also shows promise. Severe limitations in the NCSS data include unknown sampling biases, ambiguous geographical precision of observation, inconsistent sampling and analysis protocols, incomplete data records, etc. Limitations of the usefulness of the NSM include high levels of multicollinearity among model output parameters, adherence to moisture modelling behavior that does not account for the complexities of preferential flow, the assumption of free-drainage in all soils modelled, the lack of a ponding routine or a realistic accounting of snow melt dynamics, as well as other limitations. These limitations may restrict the results of this study from providing firm conclusions, but exploratory analysis does indicate some positive correlations between atmospheric climate and soil properties, particularly after atmospheric datasets are applied to simulation of soil climate through the NSM. (Abstract shortened by ProQuest.)^
Brad C. Joern, Purdue University, Phillip R. Owens, Purdue University.
Climate change|Soil sciences