EPA'S BASINS MODEL: GOOD SCIENCE OR SERENDIPITOUS MODELING?1

ABSTRACT: Better Assessment Science Integrating Point and Non‐point Sources (BASINS) is a geographic‐based watershed assessment tool developed by EPA's Office of Water to help states more efficiently target and evaluate water‐bodies that are not meeting water quality standards. BASINS (EPA, 1996a, 1998) brings together data on water quality and quantity, land uses, point source loadings, and other related spatial data with supporting nonpoint and water quality models at a quicker and more effective pace. EPA developed BASINS, to better integrate point and nonpoint source water quality assessments for the Nation's 2100+ watersheds. In its zeal to achieve this endpoint, EPA has initiated a simplistic approach that was expected to grow through scientific enhancements as TMDL developers become more familiar with modeling requirements. BASINS builds upon federal databases of water quality conditions and point source loadings for numerous parameters where quality assurance is suspect in some cases. Its design allows comprehensive assessments and modeling in typical Total Maximum Daily Load (TMDL) computations. While the TMDL utility is the primary reason BASINS was developed, other longer‐range water quality assessments will become possible as the Agency expands the suite of assessment models and databases in future releases. The simplistic approach to modeling and user‐friendly tools gives rise, however, to technical and philosophical concerns related to default data usage. Seamless generation of model input files and the failure of some utilities to work properly suggest to NCASI that serious problems may still exist and prompts the need for a more rigorous peer‐review. Furthermore, sustainable training becomes paramount, as some older modelers will be unfamiliar with Geographic Information System (GIS) technology and associated computer skills. Overall, however, BASINS was judged to be an excellent beginning tool to meet the complex environmental modeling needs in the 21st Century.     

FOG AND ACIDIFICATION IMPACTS ON ION BUDGETS OF BASINS IN NOVA SCOTIA,CANADA1

ABSTRACT: We examined hydrogeochemical records for a dozen watersheds in and near Kejimkujik National Park in southwestern Nova Scotia by relating stream ion concentrations and fluxes to atmospheric deposition, stream type (lake inlet versus outlet; brown versus clear water), and watershed type (catchment area, topography, soils, and dominant forest cover type). We found that fog and dry deposition make important contributions to S, N, Cl, H, Ca, Mg, K, and Na inputs into these watersheds. Seasalt chloride deposition from rain, snow, fog, and dry deposition equal total stream outputs on a region‐wide basis. Chloride outputs, however, differ among watersheds by a factor of about two, likely due to local differences in air flow and vegetational fog interception. We found that most of the incoming N is absorbed by the vegetation, as stream water NO3^‐ and NH4⁺ are very low. Our results also show that the vegetation and the soils absorb about half of the incoming SO4². In comparison with other North American watersheds with similar forest vegetation, Ca outputs are low, while Mg and K outputs are similar to other regions. Soil exchangeable Ca and soil cation exchange capacity are also very low. We found that first‐order forest streams with no upstream lakes have a distinct seasonal pattern that neither corresponds with the seasonal pattern of atmospheric deposition, nor with the seasonal pattern of downstream lake outlets.     

River Basin Export Reduction Optimization Support Tool; a tool to screen options for reducing nutrient loads while minimizing cost

Excess loading of nitrogen and phosphorus to river networks causes environmental harm, but reducing loads from large river basins is difficult and expensive. We developed a new tool, the River Basin Export Reduction Optimization Support Tool (RBEROST) to identify the least-cost combinations of management practices that will reduce nutrient loading to target levels in downstream and mid-network waterbodies. We demonstrate the utility of the tool in a case study in the Upper Connecticut River Basin in New England, USA. The total project cost of optimized lowest-cost plans ranged from $18.0 million to $41.0 million per year over 15 years depending on user specifications. Plans include both point source and non-point source management practices, and most costs are associated with urban stormwater practices. Adding a 2% margin of safety to loading targets improved the estimated probability of success from 37.5% to 99%. The large spatial scale of RBEROST, and the consideration of both point and non-point source contributions of nutrients, make it well suited as an initial screening tool in watershed planning.     

Modeling Changes to Streamflow,Sediment, and Nutrient Loading from Land Use Changes Due to Potential Natural Gas Development

Natural gas development using hydraulic fracturing has many potential environmental impacts, but among the most certain is the land disturbance required to build the well pads and other infrastructure required to drill and extract the gas. We used the Soil and Water Assessment Tool (SWAT) model to investigate how natural gas development could impact streamflow and sediment, total nitrogen (TN), and total phosphorous (TP) loadings in the upper Delaware River Basin (DRB), a relatively undeveloped watershed of 7,950 km² that lies above the Marcellus Shale formation. If gas development was permitted, our projections show the DRB could experience development of over 600 well pads to extract natural gas at build out, which, with supporting infrastructure (roads, gathering pipelines), could convert over 5,000 ha from existing land uses in the study area. In subbasins with development activity we found sediment, TN, and TP yields could increase by an average of 15, 0.08, and 0.03 kg/ha/yr, respectively (an increase of 2, 3, and 15%, respectively) for each one percent of subbasin land area converted into natural gas infrastructure. At the study area outlet on the Delaware River at Port Jervis, New York, we found increases in the annual average streamflow and sediment, nitrogen, and phosphorus loads of up to 0.01, 0.2, 0.2, and 1%, respectively, for a rapid development year, and 0.08, 1.3, 2.0, and 11%, respectively, for the full development scenario. Editor's note : This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

Spatiotemporal Variability of Snow Depletion Curves Derived from SNODAS for the Conterminous United States, 2004‐2013

Assessment of water resources at a national scale is critical for understanding their vulnerability to future change in policy and climate. Representation of the spatiotemporal variability in snowmelt processes in continental‐scale hydrologic models is critical for assessment of water resource response to continued climate change. Continental‐extent hydrologic models such as the U.S. Geological Survey National Hydrologic Model (NHM) represent snowmelt processes through the application of snow depletion curves (SDCs). SDCs relate normalized snow water equivalent (SWE) to normalized snow covered area (SCA) over a snowmelt season for a given modeling unit. SDCs were derived using output from the operational Snow Data Assimilation System (SNODAS) snow model as daily 1‐km gridded SWE over the conterminous United States. Daily SNODAS output were aggregated to a predefined watershed‐scale geospatial fabric and used to also calculate SCA from October 1, 2004 to September 30, 2013. The spatiotemporal variability in SNODAS output at the watershed scale was evaluated through the spatial distribution of the median and standard deviation for the time period. Representative SDCs for each watershed‐scale modeling unit over the conterminous United States (n = 54,104) were selected using a consistent methodology and used to create categories of snowmelt based on SDC shape. The relation of SDC categories to the topographic and climatic variables allow for national‐scale categorization of snowmelt processes.     

Telemetric System for Hydrology and Water Quality Monitoring in Watersheds of Northern New Mexico, Usa

This study utilized telemetric systems to sample microbes and pathogens in forest, burned forest, rangeland, and urban watersheds to assess surface water quality in northern New Mexico. Four sites included remote mountainous watersheds, prairie rangelands, and a small urban area. The telemetric system was linked to dataloggers with automated event monitoring equipment to monitor discharge, turbidity, electrical conductivity, water temperature, and rainfall during base flow and storm events. Site data stored in dataloggers was uploaded to one of three types of telemetry: 1) radio in rangeland and urban settings; 2) a conventional phone/modem system with a modem positioned at the urban/forest interface; and 3) a satellite system used in a remote mountainous burned forest watershed. The major variables affecting selection of each system were site access, distance, technology, and cost. The systems were compared based on operation and cost. Utilization of telecommunications systems in this varied geographic area facilitated the gathering of hydrologic and water quality data on a timely basis.     

POLYNOMIAL-BASED DISAGGREGATION OF HOURLY RAINFALL FOR CONTINUOUS HYDROLOGIC SIMULATION1

Hydrologic modeling of urban watersheds for designs and analyses of stormwater conveyance facilities can be performed in either an event-based or continuous fashion. Continuou simulation requires, among other things, the use of a time series of rainfall amounts. However, for urban drainage basins, which are typically small, the temporal resolution of the rainfall time series must be quite fine, and often on the order of 5 to 15 minutes. This poses a significant challenge because rainfall-gauging records are usually kept only for hourly or longer time steps. The time step sizes in stochastic rainfall generators are usually also too large for application to urban runoff modeling situations. Thus, there is a need for methods by which hourly rainfall amounts can be disaggregated to shorter time intervals. This paper presents and compares a number of approaches to this problem, which are based on the use of polynomial approximating functions. Results of these evaluations indicate that a desegregation method presented by Ormsbee (1989) is a relatively good performer when storm durations are short (2 hours), and that a quadratic spline-based approach is a good choice for longer-duration storms. Based on these results, the Ormsbee technique is recommended because it provides good performance, and can be applied easily to long time series of precipitation records. The quadratic spline-based approach is recommended as a close second choice because it performed the best most consistently, but remains more difficult to apply than the Ormsbee technique. Results of this study also indicate that, on average, all of the disaggregation methods evaluated introduce a severe negative bias into maximum rainfall intensities. This is cause for some well-justified concern, as the characteristics of runoff hydrographs are quite sensitive to maximum storm intensities. Thus, there is a need to continue the search for simple yet effective hourly rainfall disaggregation methods.     

Ohio's Balanced Growth Program: a case study of collaboration for planning and policy design

This paper describes the collaborative planning process for a new landscape planning programme in Ohio that seeks to influence land urbanisation patterns through joint local land use decision making on a watershed basis. The programme was developed through a collaborative process by a state agency-appointed task force that included agency staff and a wide range of stakeholders. The paper describes the process in terms of the collaborative mechanisms, the participants, the programmatic outputs, and the social and organisational outcomes that set the foundation for enhanced watershed quality through better land use decision-making practices. Key collaborations formed during the process were inter-agency collaborations, a non-profit organisation that partnered with the agencies, and that of state agencies with local governments to develop watershed-based land use plans. A most critical outcome was creation of a learning community, through an exploratory research process that used multiple methods of data gathering and consensus-building deliberation. The paper is based on a review of published documents and plans, meeting minutes, participant observation of committee and workgroup meetings and interactive research.     

Predicting Debris Yield From Burned Watersheds: Comparison of Statistical and Artificial Neural Network Models1

Abstract: Alluvial fans in southern California are continuously being developed for residential, industrial, commercial, and agricultural purposes. Development and alteration of alluvial fans often require consideration of mud and debris flows from burned mountain watersheds. Accurate prediction of sediment (hyper‐concentrated sediment or debris) yield is essential for the design, operation, and maintenance of debris basins to safeguard properly the general population. This paper presents results based on a statistical model and Artificial Neural Network (ANN) models. The models predict sediment yield caused by storms following wildfire events in burned mountainous watersheds. Both sediment yield prediction models have been developed for use in relatively small watersheds (50‐800 ha) in the greater Los Angeles area. The statistical model was developed using multiple regression analysis on sediment yield data collected from 1938 to 1983. Following the multiple regression analysis, a method for multi‐sequence sediment yield prediction under burned watershed conditions was developed. The statistical model was then calibrated based on 17 years of sediment yield, fire, and precipitation data collected between 1984 and 2000. The present study also evaluated ANN models created to predict the sediment yields. The training of the ANN models utilized single storm event data generated for the 17‐year period between 1984 and 2000 as the training input data. Training patterns and neural network architectures were varied to further study the ANN performance. Results from these models were compared with the available field data obtained from several debris basins within Los Angeles County. Both predictive models were then applied for hind‐casting the sediment prediction of several post 2000 events. Both the statistical and ANN models yield remarkably consistent results when compared with the measured field data. The results show that these models are very useful tools for predicting sediment yield sequences. The results can be used for scheduling cleanout operation of debris basins. It can be of great help in the planning of emergency response for burned areas to minimize the damage to properties and lives.     

Groundwater Level Trends and Drivers in Two Northern New England Glacial Aquifers

We evaluated long‐term trends and predictors of groundwater levels by month from two well‐studied northern New England forested headwater glacial aquifers: Sleepers River, Vermont, 44 wells, 1992‐2013; and Hubbard Brook, New Hampshire, 15 wells, 1979‐2004. Based on Kendall Tau tests with Sen slope determination, a surprising number of well‐month combinations had negative trends (decreasing water levels) over the respective periods. Sleepers River had slightly more positive than negative trends overall, but among the significant trends (p < 0.1), negative trends dominated 67 to 40. At Hubbard Brook, negative trends outnumbered positive trends by a nearly 2:1 margin and all seven of the significant trends were negative. The negative trends occurred despite generally increasing trends in monthly and annual precipitation. This counterintuitive pattern may be a result of increased precipitation intensity causing higher runoff at the expense of recharge, such that evapotranspiration demand draws down groundwater storage. We evaluated predictors of month‐end water levels by multiple regression of 18 variables related to climate, streamflow, snowpack, and prior month water level. Monthly flow and prior month water level were the two strongest predictors for most months at both sites. The predictive power and ready availability of streamflow data can be exploited as a proxy to extend limited groundwater level records over longer time periods.