The Influence of Soil Moisture Upon the Geothermal Climate Signal

Estimates of regional climate warming over the past few hundred years are being obtained from profiles of borehole temperature versus depth. The assumptions in recovering mean annual Surface Air Temperature (SAT) are that the relationship between the Ground Surface Temperature (GST) and the temperature-depth profile is purely conductive, and that SAT is uniquely coupled to GST. While these assumptions have been demonstrated to be approximately valid, they ignore the role of moisture transport in soil between soil and atmosphere. In this study we examine the influence of climatic changes in precipitation upon mean annual GST with climatic SAT held constant. We use the most recent version of our Prairie SVAT model for a set of 80 years simulations. Our findings are 10 increasing precipitation reduces mean annual GST, 2) phasing maximum precipitation to occur during the warmest months reduces mean annual GST, and 3) increasing the variance of precipitation reduces mean annual GST. The amplitudes of the effects are small but potentially not insignificant fractions of the geothermal climate signal. One of the long-term objectives of this investigation is to use global EOS SAT and remotely sensed soil moisture to link region-specific, geothermal climate signal histories to evolution of regional climate

Advanced Computer Technologies for Integrated Agro-Hydrologic Systems Modeling: Coupled Crop and Hydrologic Models for Agricultural Intensification Impacts Assessment

Coupling hydrologic and crop models is increasingly becoming an important task when addressing agro-hydrologic systems studies. Either for resources conservation or cropping systems improvement, the complex interactions between hydrologic regime and crop management components requires an integrative approach in order to be fully understood. Nevertheless, the literature offers limited resources on models’ coupling that targets environmental scientists. Indeed, major of guides are are destined primarily for computer specialists and make them hard to encompass and apply. To address this gap, we present an extensive research to crop and hydrologic models coupling that targets earth agro-hydrologic modeling studies in its integrative complexity. The primary focus is to understand the relationship between agricultural intensification and its impacts on hydrologic balance. We provided documentations, classifications, applications and references of the available technologies and trends of development. We applied the results of the investigation by coupling the DREAM hydrologic model with DSSAT crop model. Both models were upgraded either on their code source (DREAM) or operational base (DSSAT) for interoperability and parallelization. The resulting model operates at a grid base and daily step. The model is applied southern Italy to analyze the effect of fertilizer application on runoff generation between 2000 and 2013. The results of the study show a significant impacts of nitrogen application on water yield. Indeed, nearly 71.5 thousand cubic-meter of rain water for every kilogram of nitrogen and per hectare is lost as a reduction of runoff coefficient. Furthermore, a significant correlation between the nitrogen applications amount and runoff is found at a yearly basis with Pearson’s coefficient of 0.93