Daniel R. Fuka

Applicability of models to predict phosphorus losses in drained fields: a review.

Most phosphorus (P) modeling studies of water quality have focused on surface runoff loses. However, a growing number of experimental studies have shown that P losses can occur in drainage water from artificially drained fields. In this review, we assess the applicability of nine models to predict this type of P loss. A model of P movement in artificially drained systems will likely need to account for the partitioning of water and P into runoff, macropore flow, and matrix flow. Within the soil profile, sorption and desorption of dissolved P and filtering of particulate P will be important. Eight models are reviewed (ADAPT, APEX, DRAINMOD, HSPF, HYDRUS, ICECREAMDB, PLEASE, and SWAT) along with P Indexes. Few of the models are designed to address P loss in drainage waters. Although the SWAT model has been used extensively for modeling P loss in runoff and includes tile drain flow, P losses are not simulated in tile drain flow. ADAPT, HSPF, and most P Indexes do not simulate flow to tiles or drains. DRAINMOD simulates drains but does not simulate P. The ICECREAMDB model from Sweden is an exception in that it is designed specifically for P losses in drainage water. This model seems to be a promising, parsimonious approach in simulating critical processes, but it needs to be tested. Field experiments using a nested, paired research design are needed to improve P models for artificially drained fields. Regardless of the model used, it is imperative that uncertainty in model predictions be assessed.

Coupling terrestrial and atmospheric water dynamics to improve prediction in a changing environment

Humans have profoundly influenced their environment. It has been estimated that nearly one-third of the global land cover has been modified while approximately 40% of the photosynthesis has been appropriated. As the interface between the subsurface and the atmosphere is altered, it is imperative that we understand the influence this alteration has in terms of changing regional and global climates. Land surface heterogeneity is sometimes a principal modulator of local and regional climates and, as such, there are potential aggregation and teleconnection effects ranging in scales from soil pores to the general atmospheric circulation when the land surface is altered across a range of scales. The human fingerprint on land surface processes is critical and must also be accounted for in the discourse on land-atmosphere coupling as it pertains to climate and global change as well as local processes such as evapotranspiration and streamflow. It is at this pivotal interface where hydrologists, atmospheric scientists and ecologists must understand how their disciplines interact and influence each other.Fluxes across the land-surface directly influence predictions of ecological processes, atmospheric dynamics, and terrestrial hydrology. However, many simplifications are made in numerical models when considering terrestrial hydrology from the view point of the atmosphere and visa-versa. While this may be a necessity in the current generation of operational models used for forecasting, it can create obstacles to the advancement of process understanding. These simplifications can limit the numerical prediction capabilities on how water partitions itself throughout all phases of the water cycle. The feedbacks between terrestrial and atmospheric water dynamics are not well understood or represented by the current generation of operational land-surface and atmospheric models. This can lead to erroneous spatial patterns and anomalous temporal persistence in land-atmosphere exchanges and atmospheric water cycle predictions. Cross-disciplinary efforts are needed not only to identify but also to quantify feedbacks between terrestrial and atmospheric water at appropriate spatiotemporal scales. This is especially true as today’s young scientists set their sights on improving process understanding and prediction skill from both research and operational models used to describe such linked systems.In recognition of these challenges, a junior faculty and early career scientist forum was recently held at the National Center for Atmospheric Research (NCAR) in Boulder, Colorado with the intent of identifying and characterizing feedback interactions, and their attendant spatial and temporal scales, important for coupling terrestrial and atmospheric water dynamics. The primary focus of this forum is on improved process understanding, rather than operational products, as the possibility of incorporating more realistic physics into operational models is computationally prohibitive. We approached the subject of improved predictability through better process understanding by focusing on the following three framework questions described and discussed below.

Technical Note: Proposing a Low-Tech, Affordable, Accurate Stream Stage Monitoring System

Streamflow data are essential for water resources planning and decision making and are routinely analyzed to determine the impacts of climate change on hydrology. Unfortunately, current stream gauges, largely the responsibility of the U.S. Geological Survey (USGS) in the U.S. and similar agencies worldwide, are expensive to install and operate and are being steadily decommissioned. Part of the solution to this problem is a low-cost stream gauging system that is simple enough to use by people with little or no formal training in environmental monitoring. In this article, a low-cost, digital camera-based stream stage monitoring system is proposed, described, and tested. As a proof-of-concept, a time series was generated by taking digital pictures of a staff gauge at 3 h intervals over several weeks at a current USGS gauging site. The image-based stage heights closely matched the USGS gauge values, although significant stage height errors were evident in a small percentage (<3%) of the images. We identified the problem as being caused by shadows and irregular lighting and proposed a protocol for eliminating these errant images. When the obviously problematic images were removed, the relative differences between the image-based stages and USGS stages were approximately 5%. The next step is to develop an on-line system for post-processing the images so that watershed networks, citizen science organizations, K-12 educational institutions, and others can engage in stream monitoring and make their data freely available. We also propose some possible next steps for determining stream cross-section and flow velocity using this low-cost camera- or image-based monitoring system.

Development of a nitrous oxide routine for the SWAT model to assess greenhouse gas emissions from agroecosystems

Greenhouse gas (GHG) emissions from agroecosystems, particularly nitrous oxide (N2O), are an increasing concern. To quantify N2O emissions from agroecosystems, which occur as a result of nitrogen (N) cycling, a new physically-based routine was developed for the Soil and Water Assessment Tool (SWAT) model to predict N2O flux during denitrification and an existing nitrification routine was modified to capture N2O flux during this process. The new routines predict N2O emissions by coupling the carbon (C) and N cycles with soil moisture/temperature and pH in SWAT. The model uses reduction functions to predict total denitrification (N2+ N2O) and partitions N2 from N2O using a ratio method. The modified SWAT nitrification routine likewise predicts N2O emissions using reduction functions. The new denitrification routine and modified nitrification routine were tested using GRACEnet data at University Park, Pennsylvania, and West Lafayette, Indiana. Results showed strong correlations between plot measurements of N2O flux and the model predictions for both test sites and suggest that N2O emissions are particularly sensitive to soil pH and soil N, and moderately sensitive to soil temperature/moisture and total soil C levels. Nitrous oxide emissions are modeled with functional relationships between soil N, C, pH, temperature and moisture levels.The model is tested against high spatial and temporal resolution GRACEnet data at two sites.The model is particularly sensitive to soil pH and soil N levels.

Coupling the short-term global forecast system weather data with a variable source area hydrologic model

Few current modeling tools are designed to predict short-term, high-risk runoff from hydrologically sensitive areas (HSAs) in watersheds. This study couples the Soil and Water Assessment Tool-Variable Source Area model with the Climate Forecast System Reanalysis model and the Global Forecast System-Model Output Statistics model short term weather forecast, to develop a HSA prediction tool designed to assist producers, landowners, and planners in identifying high-risk areas generating storm runoff and pollution. Short-term predictions for stream flow and soil moisture level were estimated in the South Fork of the Shenandoah river watershed. Daily volumetric flow forecasts were found to be satisfactory four days into the future, and distributed model predictions accurately captured sub-field scale HSAs. The model has the potential to provide valuable forecasts that can be used to improve the effectiveness of agricultural management practices and reduce the risk of non-point source pollution. A watershed model-Global Forecast System model are coupled to develop a critical source area prediction tool.All software is open source and the application applies globally.The tool predicts watershed discharge and spatially distributed soil moisture conditions 14 days into the future.The tool is designed to assist land managers in identifying high-risk areas generating storm runoff and pollution.