State Water Resources Research Institute Program
Project ID: 2012IA215G
Title: Watershed scale water cycle dynamics in intensively managed landscapes: bridging the knowledge gap to support climate mitigation policies
Project Type: Research
Start Date: 9/01/2012
End Date: 8/31/2014
Congressional District: IA-2nd
Focus Categories: Agriculture, Hydrology, Water Quantity
Keywords: Agriculture Surface Runoff and Agricultural Tile Drainage; Land Use/ Land Cover (LU/LC) Changes and Urban Development, Watershed Modeling/Adaptive Watershed Management Strategies, Extreme events
Principal Investigators: Papanicolaou, Thanos N ( University of Iowa); Schilling, Keith Edwin (Iowa Geological Survey); Schnoebelen, Doug (University of Iowa); Wilson, Christopher (University of Iowa)
Federal Funds: $ 154,513
Non-Federal Matching Funds: $ 155,554
Abstract: Recent flooding in the U.S. Midwest has produced dramatic scenes of inundated farmlands and cities. Current watershed management plans to help mitigate floods call for installing Best Management Practices (BMPs), such as grassed waterways and retention basins, which reduce runoff. Yet, assessment of these BMPs is complicated by lag times between their implementation and an observed response, which can vary up to decades depending on the quantity (surface-subsurface flow, sediment, nutrients). Moreover, current BMPs may no longer be suitable to mitigate flooding, since the conditions for which they were developed have changed due to intensifying land management for elevated food/ fuel demands, extensive tiling, shifting populations, and climate shifts. Last but not least, most methods for assessing BMP effectiveness are not adequate. Field-based monitoring, for example, can be insightful but is too limited in scale and scope to analyze properly how land management and climate affect water cycle dynamics at the watershed scale. Clearly, there is a need for additional approaches to assess the type, number, and location of BMPs within a watershed for flood mitigation.
Computer models may be an alternative form of adaptive management for evaluating watershed responses to different combinations of current and projected BMPs. Models can identify critical locations in terms of flooding contributions, quantify efficiencies for a full range of possible BMPs that reduce runoff under different land uses and climates, and route the flow across scales. Undoubtedly, models can make the entire BMP design/ assessment process more cost effective and more likely to succeed. However, current modeling approaches remain fragmented and isolated, either being performed at the field- or watershed-scale. Field-scale models, alone, cannot capture adequately the collective effects that specific BMPs have on downstream flows at the watershed scale due to routing limitations, while large-scale models, individually, cannot predict the optimal type, number, and location of needed BMPs within a field or sub-watershed due to lumping effects of watershed properties.
To address these limitations, our proposed methodology involves a two-way, iterative approach that couples large- and field-scale models with GIS tools to identify the optimal type, number, and location of BMPs for reducing flooding in a representative, mixed agricultural-urban watershed of the Midwest, namely Clear Creek, IA (HUC-10). Our overarching goal is to develop an integrative suite of established models that account for the interplay between land management and climate on water cycle dynamics in rapidly changing Midwestern landscapes. We will first apply a Top-Down Approach using the Soil and Water Assessment Tool (SWAT) to identify critical sub-watersheds in CCW regarding their contributions to downstream flooding. We will then use a Bottom-Up Approach consisting of the geospatial version of the Water Erosion Prediction Project, or GeoWEPP), to distribute spatially and assess specific BMPs within the critical sub-watersheds. As part of the iterative nature of our methodology, we will "feed" the runoff volumes predicted by GeoWEPP back into SWAT to scale-up our field-scale results to the watershed scale and project downstream the BMP effectiveness. However, our modeling results for the different BMPs, even if they are technically sound and optimally designed, will never pay out in conservation benefits unless the stakeholders buy into and adopt them as policy. For this reason, we will use an Evolutionary Algorithm for identifying a spectrum of feasible management plans for accommodating multiple, competing stakeholder interests. The key products from this study include the development of adaptive management strategies that identify optimal BMP combinations for addressing multiple stakeholder concerns and a matrix rating the performance of different BMPs in terms of flow response for the Midwest. Overall, this research will improve water budget estimations in a representative, mixed agricultural-urban watershed of the Midwest under different land management and climates.