Year Established: 2013 Start Date: 2013-03-01 End Date: 2014-11-30
Total Federal Funds: $18,000 Total Non-Federal Funds: $36,000
Principal Investigators: Aris Georgakakos, Martin Kistenmacher
Abstract: Water resources planning and management studies commonly use observed streamflow data to evaluate the effects of alternative development and management plans. In many watersheds, however, observed streamflow records do not reflect natural conditions as they are affected by human water use activities such as withdrawals, returns, groundwater pumping, water transfers, reservoir operations, and land use change. These activities progressively alter the magnitude and timing of natural streamflows, making it difficult to establish a consistent hydrologic baseline to assess the true merits and impacts of alternative development and management strategies. Unimpaired flows (UIF) represent historical streamflows that have been processed to remove human influences as much as possible. While removing all human influences is practically impossible, the UIF generation process aims to approximate the natural watershed response better than direct gage measurements and create a more objective and temporally consistent basis for planning and management decisions. However, the process of reconstructing UIFs from observed records may introduce artificial uncertainties. Since UIFs are used by water management models and ecological assessments, it is possible that uncertainties and errors in the UIFs can be passed on to the results of such models and studies. Thus, prior to their use in water resources planning and management studies, UIFs should be evaluated to ensure that they do not contain large, systematic errors that can potentially misinform the planning and management process. The U.S. Army Corps of Engineers (USACE), Mobile District, has previously developed UIF datasets for the Apalachicola-Chattahoochee-Flint (ACF) river basin. While the datasets have been used in several water management and planning studies, a recent study (Georgakakos and Kistenmacher, 2012) identified several systematic and random errors in the current UIF dataset for the ACF basin. Furthermore, water management model runs revealed that uncertainties in the UIFs may lead to uncertainties in water planning and management metrics. This has important implications for all stakeholders in the ACF basin, including local, state, and federal agencies, since errors and uncertainties can potentially misinform the planning and management process. The goals of the proposed project are to (a) make improvements to the existing UIF dataset to remove some of the identified systematic error sources, to (b) further evaluate the effect that remaining uncertainties could have on water planning and management metrics, and to (c) disseminate the findings to stakeholders so that the results can be used to support water planning and management activities in the ACF basin. By addressing some of the shortcoming of the existing UIFs, the improved dataset will mitigate potential biases that could enter the water planning and management process due to incorrectly estimated UIFs and make stakeholders more confident that analyses and assessments based on the UIFs are as accurate as possible.