Year Established: 2017 Start Date: 2017-03-01 End Date: 2018-02-28
Total Federal Funds: $27,500 Total Non-Federal Funds: $55,000
Principal Investigators: Nicholas Engdahl, Alexandra Richey
Abstract: One of the challenges facing many parts of the world is how to effectively plan the management of both surface water and groundwater resources for the future. Groundwater is often falsely viewed as an infinite resource, providing an assumed permanent buffer when preferred surface water resources become unavailable. Even where groundwater is known to be finite, a lack of available monitoring tools leads to de facto management practices that encourage minimally regulated and unsustainable groundwater use. As a result, largescale depletion has been occurring in aquifer systems globally, including in Washington State. Climate stresses will further this decline if left unmitigated as dependence on groundwater increases in response to decreasing reliability of surface supplies. Many factors complicate the development of a sustainable groundwater management framework but none are more restrictive than the limited data availability and associated lack of an accurate inventory of groundwater stocks and flows. Groundwater can only be directly quantified by observing water levels in monitoring wells but these only provide a local picture, and are often scattered in time and space if they are available at all. Recent advances in remote sensing technology provide a large-scale picture of terrestrial hydrologic change, but these data require process level understanding to separate different components of the hydrologic cycle. Interpretations based on either data source can be improved using process-based, integrated hydrologic models to test our understanding of the system, but, in order to ensure accuracy, the scale of the models must be congruent with the scale of the data used to calibrate it. Thus, the raw point data may be unreliable for regional scale modeling and the satellite data cannot be easily related to monitoring well observations. The central problem is that the scales of the available observations are vastly different than the scales needed for planning. This proposal aims to bridge the gap between the scale of observations and the scale of planning models by quantifying the small-scale variability of hydrologic processes and sequentially upscaling hydrologic models to larger scales. This will connect point scale measurements to larger scale observations by developing scaling laws for the input parameters and the output variables of integrated models. The laws will enable meaningful, larger-scale simulations that can steadily increase in spatial extents that will ultimately combine the monitoring well and satellite data with dynamic integrated modeling tools. The small-scale model will be developed at the farm-scale using high-resolution data provided from a Long-Term Agroecological Reserve (LTAR) within the Palouse region of Eastern Washington. The scaling laws developed from this site will be used to construct a preliminary basin-scale model of the entire Palouse Basin aquifer that will be compared to global datasets. The results will allow confident, data-driven simulations of integrated hydrologic processes at increasingly larger scales. The project team intends to translate these advances into a sustainable management model for the greater Columbia Plateau Regional Aquifer System in future research that will also incorporate remote sensing observations for a robust cross-scale approach.