A SPATIAL DECISION SUPPORT SYSTEM FOR ASSESSING AGRICULTURAL NONPOINT SOURCE POLLUTION1

ABSTRACT: A spatial decision support system (SDSS) was developed to assess agricultural nonpoint source (NPS) pollution using an NPS pollution model and geographic information systems (GIS). With minimal user interaction, the SDSS assists with extracting the input parameters for a distributed parameter NPS pollution model from user-supplied GIS base layers. Thus, significant amounts of time, labor, and expertise can be saved. Further, the SDSS assists with visualizing and analyzing the output of the NPS pollution simulations. Capabilities of the visualization component include displays of sediment, nutrient, and runoff movement from a watershed. The input and output interface techniques/algorithms used to develop the SDSS, along with an example application of the SDSS, are described.     

Forecasting land use change and its environmental impact at a watershed scale

Urban expansion is a major driving force altering local and regional hydrology and increasing non-point source (NPS) pollution. To explore these environmental consequences of urbanization, land use change was forecast, and long-term runoff and NPS pollution were assessed in the Muskegon River watershed, located on the eastern coast of Lake Michigan. A land use change model, LTM, and a web-based environmental impact model, L-THIA, were used in this study. The outcomes indicated the watershed would likely be subjected to impacts from urbanization on runoff and some types of NPS pollution. Urbanization will slightly or considerably increase runoff volume, depending on the development rate, slightly increase nutrient losses in runoff, but significantly increase losses of oil and grease and certain heavy metals in runoff. The spatial variation of urbanization and its impact were also evaluated at the subwatershed scale and showed subwatersheds along the coast of the lake and close to cities would have runoff and nitrogen impact. The results of this study have significant implications for urban planning and decision making in an effort to protect and remediate water and habitat quality of Muskegon Lake, which is one of Lake Michigan's Areas of Concern (AOC), and the techniques described here can be used in other areas.     

Development of the Spatial Rainfall Generator (SRGEN) for the Agricultural Policy/Environmental Extender Model

Accurate spatial representation of climatic patterns is often a challenge in modeling biophysical processes at the watershed scale, especially where the representation of a spatial gradient in rainfall is not sufficiently captured by the number of weather stations. The spatial rainfall generator (SRGEN) is developed as an extension of the “weather generator” (WXGEN), a component of the Agricultural Policy/Environmental eXtender (APEX) model. SRGEN generates spatially distributed daily rainfall using monthly weather statistics available at multiple locations in a watershed. The spatial rainfall generator as incorporated in APEX is tested on the Cowhouse watershed (1,178 km²) in central Texas. The watershed presented a significant spatial rainfall gradient of 2.9 mm/km in the lateral (north‐south) directions based on four rainfall gages. A comparative analysis between SRGEN and WXGEN indicates that SRGEN performs well (PBIAS = 2.40%). Good results were obtained from APEX for streamflow (NSE = 0.99, PBIAS = 8.34%) and NO₃‐N and soluble P loads (PBIAS ≈ 6.00% for each, respectively). However, APEX underpredicted sediment yield and organic N and P loads (PBIAS: 24.75‐27.90%) with SRGEN, although its uncertainty in output was lower than WXGEN results (PBIAS: ?13.02 to ?46.13%). The overall improvement achieved in rainfall generation by SRGEN is demonstrated to be effective in the improving model performance on flow and water quality output.     

WATERSHEDSS: A DECISION SUPPORT SYSTEM FOR WATERSHED-SCALE NONPOINT SOURCE WATER QUALITY PROBLEMS1

ABSTRACT: A significant portion of all pollutants entering surface waters (streams, lakes, estuaries, and wetlands) derives from non-point source (NPS) pollution and, in particular, agricultural activities. The first step in restoring a water resource is to focus on the primary water quality problem in the watershed. The most appropriate NPS control measures, which include best management practices (BMPs) and landscape features, such as wetlands and riparian areas, can then be selected and positioned to minimize or mitigate the identified pollutant(s). A computer-based decision sup. port and educational software system, WATERSHEDSS (WATER, Soil, and Hydro-Environmental Decision Support System), has been developed to aid managers in defining their water quality problems and selecting appropriate NPS control measures. The three primary objectives of WATERSHEDSS are (1) to transfer water quality and land treatment information to watershed managers in order to assist them with appropriate land management/land treatment decisions; (2) to assess NPS pollution in a watershed based on user-supplied information and decisions; and (3) to evaluate, through geographical information systems-assisted modeling, the water quality effects of alternative land treatment scenarios. WATERSHEDSS is available on the World Wide Web (Web) at http://h2osparc.wq.ncsu.edu .     

A Hydrologic/Water Quality Model Applicati11

Abstract: This paper presents a procedure for standard application of hydrologic/water quality models. To date, most hydrologic/water quality modeling projects and studies have not utilized formal protocols, but rather have employed ad hoc approaches. The procedure proposed is an adaptation and extension of steps identified from relevant literature including guidance provided by the U.S. Environmental Protection Agency. This protocol provides guidance for establishing written plans prior to conducting modeling efforts. Eleven issues that should be addressed in model application plans were identified and discussed in the context of hydrologic/water quality studies. A graded approach for selection of the level of documentation for each item was suggested. The creation and use of environmental modeling plans is increasingly important as the results of modeling projects are used in decision‐making processes that have significant implications. Standard modeling application protocols similar to the proposed procedure herein provide modelers with a roadmap to be followed, reduces modelers’ bias, enhances the reproducibility of model application studies, and eventually improves acceptance of modeling outcomes.     

RUNOFF MODELING OF A MOUNTAINOUS CATCHMENT USING TOPMODEL: A CASE STUDY1

ABSTRACT: The rainfall‐runoff response of the Tygarts Creek Catchment in eastern Kentucky is studied using TOPMODEL, a hydrologic model that simulates runoff at the catchment outlet based on the concepts of saturation excess overland flow and subsurface flow. Unlike the traditional application of this model to continuous rainfall‐runoff data, the use of TOPMOEL in single event runoff modeling, specifically floods, is explored here. TOPMODEL utilizes a topographic index as an indicator of the likely spatial distribution of rainfall excess generation in the catchment. The topographic index values within the catchment are determined using the digital terrain analysis procedures in conjunction with digital elevation model (DEM) data. Select parameters in TOPMODEL are calibrated using an iterative procedure to obtain the best‐fit runoff hydrograph. The calibrated parameters are the surface transmissivity, T_O, the transmissivity decay parameter, m, and the initial moisture deficit in the root zone, S_r0. These parameters are calibrated using three storm events and verified using three additional storm events. Overall, the calibration results obtained in this study are in general agreement with the results documented from previous studies using TOPMODEL. However, the parameter values did not perform well during the verification phase of this study.     

MACROSCALE HYDROLOGIC MODELING FOR REGIONAL CLIMATE ASSESSMENT STUDIES IN THE SOUTHEASTERN UMTED STATES1

ABSTRACT: A macroscale hydrologic model is developed for regional climate assessment studies under way in the southeastern United States. The hydrologic modeling strategy is developed to optimize spatial representation of basin characteristics while maximizing computational efficiency. The model employs the “grouped response unit” methodology, which follows the natural drainage pattern of the area. First order streams are delineated and their surface characteristics are tested so that areas with statistically similar characteristics can be combined into larger computational zones for modeling purposes. Hydrologic response units (HRU) are identified within the modeling units and a simple three‐layer water balance model, Soil and Water Assessment Tool (SWAT), is executed for each HRU. The runoff values are then convoluted using a triangular unit hydrograph and routed by Muskingum‐Cunge method. The methodology is shown to produce accurate results relative to other studies, when compared to observations. The model is used to evaluate the potential error in hydrologic assessments when using GCM predictions as climatic input in a rainfall‐runoff dominated environment. In such areas, the results from this study, although limited in temporal and spatial scope, appear to imply that use of GCM climate predictions in short term quantitative analyses studies in rainfall‐runoff dominated environments should proceed with caution.     

A MODELING APPROACH FOR OPTIMUM SEDIMENT DETENTION POND DESIGN1

ABSTRACT: A design procedure to determine optimum size for a sediment detention pond is presented. The procedure is based on simulating the sediment removal efficiency of the pond in conjunction with temporal variations in rainfall and potential land use and/or management options. The simulation procedure is based on a combined probabilistic-deterministic modeling approach. The probabilistic model generates daily rainfall with hourly increments for a selected site. The deterministic model simulates sediment yield and concentration for drainage area (pond inflow) and sediment trapping efficiency of the pond. The sediment yield and concentration in pond effluent is estimated from the difference between sediment inflow to the pond and sediment trapped by the pond. As an example, the procedure is applied to determine optimum design for a sediment detention pond in a surface mined area using several pond design options and alternative mining operation/land reclamation strategies.     

HYDROLOGIC MODELING OF LAND PROCESSES IN PUERTO RICO USING REMOTELY SENSED DATA1

ABSTRACT: An integrated, multi-disciplinary effort to model land processes affecting Mayaguez Bay in western Puerto Rico is described. A modeling strategy was developed to take advantage of remotely sensed data. The spatial, interannual, and seasonal variability of sediment discharges to the bay were also evaluated. Classified images of remotely sensed data revealed the spatial distribution and quantities of land use classes in the region and aided in the discretization of the watershed into homogeneous regions. These regions were modeled using a geomorphic modeling technique based upon spatially averaged parameters. Simulation results from the modeling effort compared favorably with observations at two locations within the watershed. Results showed that runoff and sediment loads from the area exhibit a marked seasonal trend and that deforested areas located in the foothill regions of the watershed contribute a disproportionate share of the sediment load to the bay. In years when rainfall distributions are uniformly distributed over the area, the sediment yields may be up to 100 percent higher than years when the rainfall is concentrated in the heavily forested mountainous regions.     

Assessing Watershed-Scale, Long-Term Hydrologic Impacts of Land-Use Change Using a GIS-NPS Model

Land-use change, dominated by an increase in urban/impervious areas, has a significant impact on water resources. This includes impacts on nonpoint source (NPS) pollution, which is the leading cause of degraded water quality in the United States. Traditional hydrologic models focus on estimating peak discharges and NPS pollution from high-magnitude, episodic storms and successfully address short-term, local-scale surface water management issues. However, runoff from small, low-frequency storms dominates long-term hydrologic impacts, and existing hydrologic models are usually of limited use in assessing the long-term impacts of land-use change. A long-term hydrologic impact assessment (L-THIA) model has been developed using the curve number (CN) method. Long-term climatic records are used in combination with soils and land-use information to calculate average annual runoff and NPS pollution at a watershed scale. The model is linked to a geographic information system (GIS) for convenient generation and management of model input and output data, and advanced visualization of model results. The L-THIA/NPS GIS model was applied to the Little Eagle Creek (LEC) watershed near Indianapolis, Indiana, USA. Historical land-use scenarios for 1973, 1984, and 1991 were analyzed to track land-use change in the watershed and to assess impacts on annual average runoff and NPS pollution from the watershed and its five subbasins. For the entire watershed between 1973 and 1991, an 18% increase in urban or impervious areas resulted in an estimated 80% increase in annual average runoff volume and estimated increases of more than 50% in annual average loads for lead, copper, and zinc. Estimated nutrient (nitrogen and phosphorus) loads decreased by 15% mainly because of loss of agricultural areas. The L-THIA/NPS GIS model is a powerful tool for identifying environmentally sensitive areas in terms of NPS pollution potential and for evaluating alternative land use scenarios for NPS pollution management.     

Nonpoint Source Pollution Responses Simulation for Conversion Cropland to Forest in Mountains by SWAT in China

Several environmental protection policies have been implemented to prevent soil erosion and nonpoint source (NPS) pollutions in China. After severe Yangtze River floods, the “conversion cropland to forest policy” (CCFP) was carried out throughout China, especially in the middle and upper reaches of Yangtze River. The research area of the current study is located in Bazhong City, Sichuan Province in Yangtze River watershed, where soil erosion and NPS pollution are serious concerns. Major NPS pollutants include nitrogen (N) and phosphorus (P). The objective of this study is to evaluate the long-term impact of implementation of the CCFP on stream flow, sediment yields, and the main NPS pollutant loading at watershed level. The Soil and Water Assessment Tool (SWAT) is a watershed environmental model and is applied here to simulate and quantify the impacts. Four scenarios are constructed representing different patterns of conversion from cropland to forest under various conditions set by the CCFP. Scenario A represented the baseline, i.e., the cropland and forest area conditions before the implementation of CCFP. Scenario B represents the condition under which all hillside cropland with slope larger than 25° was converted into forest. In scenario C and D, hillside cropland with slope larger than 15° and 7.5° was substituted by forest, respectively. Under the various scenarios, the NPS pollution reduction due to CCFP implementation from 1996–2005 is estimated by SWAT. The results are presented as percentage change of water flow, sediment, organic N, and organic P at watershed level. Furthermore, a regression analysis is conducted between forest area ratio and ten years’ average NPS load estimations, which confirmed the benefits of implementing CCFP in reducing nonpoint source pollution by increasing forest area in mountainous areas. The reduction of organic N and organic P is significant (decrease 42.1% and 62.7%, respectively) at watershed level. In addition, this study also proves that SWAT modeling approach can be used to estimate NPS pollutants’ impacts of land use conversions in large watershed.     

AVGWLF‐Based Estimation of Nonpoint Source Nitrogen Loads Generated Within Long Island Sound Subwatersheds1

Abstract: The Generalized Watershed Loading Functions (GWLF) model and its ArcView interface (AVGWLF) were used to estimate and examine the components of the total nitrogen (TN) nonpoint source (NPS) load generated within New York and Connecticut (CT) watersheds surrounding Long Island Sound (LIS, the Sound). The majority of data used as model inputs were generally available from online sources, and the work involved an overall calibration to streamflow and TN data in accordance with generic guidelines recommended in the GWLF manual. The GWLF model performance for three calibration and two validation watersheds in CT was compared with results of a detailed model, Hydrological Simulation Program in Fortran, developed in a previous study. The results of the application illustrate the usefulness of the relatively simpler, less parameter‐intensive GWLF model in performing exploratory loading analysis in preparation for adaptive nutrient management in the LIS watersheds. The presented methodology is valuable for identification of priority watersheds for NPS pollution reduction and also for planning‐level evaluation of best management practices to achieve the desired reductions. It is estimated that ground‐water base flow may be the largest pathway for NPS TN to the Sound, contributing about 54% of the total NPS TN load, a finding with significant implications for LIS total maximum daily load reduction scenarios. In addition to ground water, septic systems are estimated to contribute about 17% of the total load, with the remaining TN load being mostly runoff from urban (17%), agricultural (5%), and low impact (e.g., forest) areas (6%).     

滨海新区大港农业非点源污染状况研究简

立足天津市滨海新区大港农业背景情况,采用适宜的方法,分别从种植业、畜禽养殖和水产养殖业三方面分析非点源污染物排放量。结果表明：2010年大港地区农业非点源主要污染物COD排放量为377.67 t,氨氮排放量为72.33 t,畜禽养殖排放的污染已占农业非点源污染的一半以上。在此基础上,分别从优化畜禽养殖的养殖模式、种植业科学施用化学品、水产养殖合理投放饲料等方面提出了大港地区农业非点源污染控制的对策。     

Stream Nitrogen Sources Apportionment and Pollution Control Scheme Development in an Agricultural Watershed in Eastern China

A modeling system that couples a land-use-based export coefficient model, a stream nutrient transport equation, and Bayesian statistics was developed for stream nitrogen source apportionment. It divides a watershed into several sub-catchments, and then considers the major land-use categories as stream nitrogen sources in each sub-catchment. The runoff depth and stream water depth are considered as the major factors influencing delivery of nitrogen from land to downstream stream node within each sub-catchment. The nitrogen sources and delivery processes are lumped into several constant parameters that were calibrated using Bayesian statistics from commonly available stream monitoring and land-use datasets. This modeling system was successfully applied to total nitrogen (TN) pollution control scheme development for the ChangLe River watershed containing six sub-catchments and four land-use categories. The temporal (across months and years) and spatial (across sub-catchments and land-use categories) variability of nonpoint source (NPS) TN export to stream channels and delivery to the watershed outlet were assessed. After adjustment for in-stream TN retention, the time periods and watershed areas with disproportionately high-TN contributions to the stream were identified. Aimed at a target stream TN level of 2 mg L^?1, a quantitative TN pollution control scheme was further developed to determine which sub-catchments, which land-use categories in a sub-catchment, which time periods, and how large of NPS TN export reduction were required. This modeling system provides a powerful tool for stream nitrogen source apportionment and pollution control scheme development at the watershed scale and has only limited data requirements.     

Long‐Term High‐Resolution Radar Rainfall Fields for Urban Hydrology

Accurate records of high‐resolution rainfall fields are essential in urban hydrology, and are lacking in many areas. We develop a high‐resolution (15 min, 1 km²) radar rainfall data set for Charlotte, North Carolina during the 2001‐2010 period using the Hydro‐NEXRAD system with radar reflectivity from the National Weather Service Weather Surveillance Radar 1988 Doppler weather radar located in Greer, South Carolina. A dense network of 71 rain gages is used for estimating and correcting radar rainfall biases. Radar rainfall estimates with daily mean field bias (MFB) correction accurately capture the spatial and temporal structure of extreme rainfall, but bias correction at finer timescales can improve cold‐season and tropical cyclone rainfall estimates. Approximately 25 rain gages are sufficient to estimate daily MFB over an area of at least 2,500 km², suggesting that robust bias correction is feasible in many urban areas. Conditional (rain‐rate dependent) bias can be removed, but at the expense of other performance criteria such as mean square error. Hydro‐NEXRAD radar rainfall estimates are also compared with the coarser resolution (hourly, 16 km²) Stage IV operational rainfall product. Stage IV is adequate for flood water balance studies but is insufficient for applications such as urban flood modeling, in which the temporal and spatial scales of relevant hydrologic processes are short. We recommend the increased use of high‐resolution radar rainfall fields in urban hydrology.     

SPATIAL ANALYSIS FOR MONTHLY RAINFALL IN SOUTH FLORIDA1

ABSTRACT: Several methods have been developed to interpolate point rainfall data and integrate areal rainfall data from any network of stations. From previous studies, it can be concluded that models for spatial analysis of rainfall are dependent on topography, area of analysis, type of rainfall, and density of gauging network. The purpose of this study is to evaluate a set of six appropriate models for point and areal rainfall estimations over a 4000 square mile area in South Florida. In this study, a case of developing spatial continuity model for monthly rainfall from a database that had various lengths of records and missing data is documented. The spatial correlation and variogram models for monthly rainfall were developed. Six methods of spatial interpolation were applied and the results validated with historical observations. The results of the study indicate that the multiquadric, kriging, and optimal interpolation schemes are the best three methods for interpolation of monthly rainfall within the study area. The optimal and kriging methods have the advantage of providing estimates of the error of interpolation. The optimal interpolation method uses the spatial correlation function and the kriging method uses the variogram function. The two spatial functions are related. Either of the two methods provide good estimates of monthly point and areal rainfall in the study area.     

Evaluation of Gauge‐Radar Merging Methods Using a Semi‐Distributed Hydrological Model in the Upper Thames River Basin,Canada

Gauge‐radar merging methods combine rainfall estimates from rain gauges and radar to capitalize on the strengths of the individual instruments. The performance of four well‐known gauge‐radar merging methods, including mean field bias correction, Brandes spatial adjustment, local bias correction using kriging, and conditional merging, are examined using Environment Canada radar and the Upper Thames River Basin in southwestern Ontario, Canada, as a case study. The analysis assesses the effect of gauge‐radar merging methods on: (1) the accuracy of predicted rainfall accumulations; and (2) the accuracy of predicted streamflows using a semi‐distributed hydrological model. In addition, several influencing factors (i.e., gauge density, storm type, basin type, proximity to the radar tower, and time‐step of adjustment) are analyzed to determine their effect on the performance of the rainfall estimation techniques. Confirming results of previous studies, the merging methods provide an increase in the accuracy of both rainfall accumulation estimations and predicted streamflows. The results also indicate specific factors such as gauge density, rainfall intensity, and time‐step of adjustment can reduce the accuracy of merging methods and play a key role in the examination of its use for operational purposes. Results provide guidance for hydrologists and engineers assessing how best to apply corrected radar products to improve rainfall estimation and hydrological modeling accuracy.     

WATERSHED OPTIMIZATION OF AGRICULTURAL BEST MANAGEMENT PRACTICES: CONTINUOUS SIMULATION VERSUS DESIGN STORMS1

ABSTRACT: Nonpoint source (NPS) models and expert opinions are often used to prescribe best management practices (BMPs) for controlling NPS pollution. An optimization algorithm (e.g., a genetic algorithm, or GA) linked with a NPS model (e.g., Annualized AGricultural Nonpoint Source pollution model, or AnnAGNPS), can be used to more objectively prescribe BMPs and to optimize NPS pollution control measures by maximizing pollutant reduction and net monetary return from a watershed. Pollutant loads from design storms and annual loads from a continuous simulation can both be used for optimizing BMP schemes. However, which strategy results in a better solution (in terms of providing water quality protection) for a watershed is not clear. The specific objective of the study was to determine the differences in watershed pollutant loads, in an experimental watershed in Pennsylvania, resulting from optimization analyses performed using pollutant loads from a series of five 2‐yr 24‐hr storm events, a series of five 5‐yr 24‐hr storm events, and cumulative pollutant loads from a continuous simulation of five years of weather data. For each of these three different event alternatives, 100 near optimal solutions (BMP schemes) were generated. Sediment (Sed), sediment nitrogen (SedN), dissolved N (SolN), sediment organic carbon (SedOC), and sediment phosphorus (SedP) loads from a different five‐year period (an evaluation period) suggest that the optimal BMP schemes resulting from the use of annual cumulative pollutant loads from a continuous simulation of five years of weather data provide smaller cumulative NPS pollutant loads at the watershed outlet.     

SELECTION AND SUSTAINABILITY OF LAND AND WATER RESOURCE MANAGEMENT SYSTEMS1

ABSTRACT: Most spatial decision support systems for natural resource planning and management are limited by their scenario-based (non-behavioral), deterministic (non-stochastic) structure. A spatial decision support system is developed that uses a multiple attribute decision-making model to explain how a property manager selects a land and water resource management system (LWRMS) based on its multiple, stochastic economic and environmental attributes. The decision support system assesses sustainable resource management at the property and watershed scales and identifies the most cost-effective policy for enhancing sustainable resource management. Economic attributes are determined with an economic model and environmental attributes are simulated with an environmental model. Input parameters for both models are generated with a geographic information system. The decision support system is used to rank five LWRMS for a sample of 20 farmers in Missouri's Goodwater Creek watershed and for two hypothetical watershed alliance groups. Results indicate that the average farmer and the two alliance groups would rank the five LWRMS in the same manner. From the viewpoint of the watershed alliance, the most preferred LWRMS for the average farmer in the watershed is sustainable.