Recipient Organization
UNIVERSITY OF FLORIDA
G022 MCCARTY HALL
GAINESVILLE,FL 32611
Performing Department
Agricultural and Biological Engineering
Non Technical Summary
Effective mitigation of nonpoint source (NPS) surface water pollution is difficult because of the many of sources, pollutants, processand stakeholders involved.Presently, water quality conservation programs designed to install best management practices (BMPs) to mitigate NPS have not been targeted to those areas of the landscape that contribute to NPS pollution disproportionately. Large reductions in watershed-level nutrient loads could be achieved through coordinated placement of BMPs on high-contributing areas.The the placement and optimization of BMPs for controlling NPS requires a suite of individual and coupled mechanistic models that effectively capture a range of factors and watershed processes. The goal of this multistate research project proposal is to explore effective solutions to predict BMP performance (individually and cumulatively) at the various spatial and temporal scales. Tools developed through this effort can be used to inform watershed management decisions and investments in the presence of pervasive and unavoidable uncertainty to achieve water quality while minimizing investment. Dr. Muñoz-Carpena's contribution to the projectwll bein 3 key areas: a) analysis of BMP (vegetative filter strips) effectiveness in different parts of the landscape using the computer model VFSMOD he developed; b) adoption of state-of-the-art and objective model evaluation methods with the tool he developed, FITEVAL; and c) systematic analysis of system uncertainty in model applications and predictions based on Global Sensitivity and Uncertianty Analysus (GSUA) with some of the new screening tools (eSU) his team developed.
Animal Health Component
30%
Research Effort Categories
Basic
20%
Applied
30%
Developmental
50%
Goals / Objectives
Develop tools that utilize both monitoring and modeling to better inform targeted BMP implementation
Advance water quantity and quality models for mixed-use watersheds
Conduct integrated assessment of uncertainty and sensitivity analysis of monitoring and modeling approaches both individually and in combination
Project Methods
Objective 1: Develop tools that utilize both monitoring and modeling to better inform targeted BMP implementation.Water quality improvements at the watershed-scale due to BMP implementations have been slow and often not as extensive as expected (Meals et al., 2010; Lee et al., 2012; Sharpley et al., 2012, 2013). The lack of progress in addressing water quantity and water quality issues could be due to the overly high expectation of long-term effectiveness of BMPs, the possible degradation of BMP effectiveness over time, and possibly BMPs themselves becoming sources of pollutants. While the need for assessing effectiveness of past and current BMPs have been widely acknowledged, implementation of novel BMPs that are targeted and precision-based is needed. Insights into the processes that determine pollutant fluxes, their fate and transport and the part that each BMP plays in pollutant reduction are needed. In addition to the process of pollutant transport and removal over time and space, it is essential to more fully understand the role that BMP targeting may have in NPS reduction, and at what scale that targeting may be most effective. In order to make this assessment, detailed information about forcing-functions, BMPs specific data, at various temporal and spatial scales are needed.Monitoring and assessment activities should be considered as an integrated program, rather than monitoring standing alone as separate and distinct, for two reasons. One is that the ultimate value of monitoring is only realized when it is coupled with assessments in which raw data are converted to information that can form the basis of actions. The second reason is that assessment is a crucial step in the adaptive management process. Findings of the assessment process, whether they are statistically robust or are only general indications, should be the trigger to modify future monitoring activities as well as future watershed management activities. Having a strong data analysis and assessment program, with a wide range of tools and products, is vital to the continued success of the BMPs implementation. The products need to include those aimed at regulatory agencies as well as the interested public, decision-makers (including those who decide about future investments of public funds), and the science community.Therefore the major task for the Objective 1 is to collect data at the BMP, field- and watershed-scale and various time scales as budget allows. Data will include water quantity, water quality (e.g. sediment, nutrients, and pathogens), pollutant removal kinetics, various climatic variables, etc. Data collection will also likely involve measuring fluxes at the soil-air, water-air, and soil-aquifer interfaces to better understand BMP performance. The targeted BMPs for this research include: agricultural and urban BMPs such as riparian buffer zones/filter strips, constructed wetlands, stream-side fencing, sediment detention practices, nutrient management, integrated pest management, denitrifying bioreactors, conservation tillage, infiltration practices, porous pavement, green roofs, grass waterways/vegetated swales, rain garden, soil carbon enhancement, etc.Objective 2: Advance water quantity and quality models for mixed-use watersheds.With monitoring data from Objective 1 and previous studies we can use physically and statistically-based predictive modeling to improve the understanding of linkages between driving variables and water-quality outcomes. The driving variables can include land uses, land-use management (e.g., cropping systems, stormwater management systems, waste disposal systems), and climatic factors such as the probability distributions of air temperatures and precipitation amounts. The water quality outcomes can be probability distributions of concentrations or fluxes of water-quality variables in surface water bodies, streams, and reservoirs. The improvement in understanding and planning of watershed management strategies is accomplished through analysis and methods that use the observed weather, hydrology, land use, and water quality data to estimate the parameters of geospatially referenced modelling tools that statistically relate the inputs to relevant water-quality outputs. Novel theoretical approaches to address watershed issues associated with emerging contaminants will need to be better integrated with present models or developed anew. Interdisciplinary cooperation with researchers, watershed specialists, agricultural producers, local stakeholders, agency personnel, etc., will need to be incorporated during the development of novel watershed modeling approaches. The purpose of such models is to better predict water quality responses to the combination of changes in climate, land use, land-use management, and conservation practice implementation that anticipate to occur short or long-term in the future. The models need to be improved to be capable of prioritizing management action plans and investments to help ensure that the highest quality of water is available for use. As the result of achieving this objective, model evaluation, model development, and data collection responsibilities for the participating states/partners will be established. The results will be shared among stakeholders as research progresses. The application of objective frameworks for the testing and evaluation of BMP modeling tools like FITEVAL (Ritter and Muñoz-Carpena, 2013) and that of Harmel et al. (2014) will be a signature feature of this proposal. Therefore the major tasks of this objective are to develop, improve and evaluate process-based models and other approaches for planning and management of mixed land use watersheds.?Objective 4: Conduct integrated assessment of uncertainty and sensitivity analysis of monitoring and modeling approaches both individually and in combination.It is essential to know how modelers present information on model prediction uncertainty and its important controlling factors so that decision makers and stakeholders are better off than they were in the absence of knowledge of this uncertainty. Uncertainty estimates corresponding to measured data can contribute to improved BMPs design and implementation, model application, and informed decision-making. While the results of the first three project objectives (1, 2 and 3) will advance our knowledge about the potential impacts of BMP implementation at various spatial and temporal scales, there still is a large knowledge gap that needs to be addressed. Reckhow (2014) presents a decision analytic approach for using model prediction uncertainty that could serve as a mental framework. This approach is based on the expected value of sample information (EVSI), which can be calculated for a model prediction accompanied by uncertainty analysis. A decision analytic approach when the cost of each land use/land cover, BMPs/model is compared to the cost of the corresponding additional monitoring and/or modeling requirement could also be used in addressing model and monitoring uncertainties. There are many tools that are now used to address monitoring (Birgand et al., 2007) and model uncertainties (Shirmohammadi et al, 2006). In particular, global sensitivity and uncertainty analysis (GSUA) (Saltelli et al., 2004, 2008; Muñoz-Carpena et al., 2007) is a powerful modern framework to include uncertainty in management applications. Therefore the major task for this objective is to analyze uncertainties associated with the monitoring and predictive models at various temporal and spatial scales using GSUA and other state-of-the-art tools. GSUA accounts for all sources of uncertainty, such as model inputs, model structure, model parameters, and measured data. We will conduct uncertainty analyses on model inputs (data from monitoring, forcing data) and outputs to produce a prediction.