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Development of spatiotemporal waste load

allocation (WLA) protocols for regulatory

water quality planning and management

J. Yoon

Department of Civil and Environmental Engineering, Old DominionUniversity, Norfolk, USA

Abstract

Water quality modeling and regulatory management guideline study wasconducted on a 1499-ha (3,700 acres) watershed in the Naval Security GroupActivity (NSGA) Northwest base at the Virginia/North Carolina border, USA.NSGA Northwest is a Navy Communications Facility on which the Navy, theMarines, the Coast Guard and the North Atlantic Treaty Organization (NATO)have facilities. The watershed has a baseline nonpoint source pollution (NFS)contributing to the Northwest River that eventually influxes to the ChesapeakeBay. Stormwater runoff discharge from the watershed influxes to the NorthwestRiver, approximately 6.44 km (4 miles) upstream of the intake from the City ofChesapeake's potable water supply. A distributed parameter water quality modeland Geographic Information System (GIS) framework was implemented todevelop a spatiotemporal waste load allocation (WLA) protocols and totalmaximum daily load (TMDL) estimation to abate the current level of NFSpollution at the same time to preserve the quality of drinking water supply at theNorthwest River. A Unix-based Arc/INFO GIS was linked to an event-based,large problem domain distributed parameter water quality model to generaterunoff synthesis and subsequent transport of pollutants and sediment from thewatershed. Linking such models to GIS can facilitate better data manipulationand analysis than conventional methods of manually and separately preparingdata. This bilateral linkage framework resulted in a powerful, up-to-date tool thatwould be capable of monitoring and instantaneously visualizing the transport ofany pollutant as well as effectively identifying critical areas of the NFS pollution.The framework was also used to simulate various "what if scenarios of event-based and background load (BL) of pollutants to develop WLA and TMDLthrough regulatory best management practice (BMP) protocols. Results showed

Management Information Systems, C.A. Brebbia & P. Pascolo (Editors) © 2000 WIT Press, www.witpress.com, ISBN 1-85312-815-5

416 Management Information Systems

that the optimal BMP scenario achieved an average reduction of about 41% insoluble and sediment-attached nitrogen and about 62% reduction in soluble andsediment phosphorous from current NFS pollution levels.

Introduction

There have been a significant interest in recent years in Waste Load Allocation(WLA) and Total Maximum Daily Loads (TMDLs) approach in the water qualitymanagement and planning areas. WLA/TMDLs are the implementation of rulesincluded in Section 303(d) of the Clean Water Act of 1972. Today, TMDLs areessentially driving the watershed approach to water quality management, theperspective that all point and nonpoint sources of pollution in a watershed as wellas the physical characteristics of the water body itself are inextricably linked. Asa result, TMDLs are aimed at managing all sources of pollution which affectbeneficial uses of water, covering both point and nonpoint sources.

Historically, point sources discharges, i.e., WLAs, from single sourcessuch as wastewater treatment plants or factories have been most closelymonitored and addressed. TMDLs, and the significant improvements in reducingpoint source pollution since the early '70s, have now shifted much of themanagement focus to nonpoint source pollution, contamination from diffusesources on the landscape such as agricultural runoff, or urban stormwater (Tyler,1992). Nonpoint source pollution involves a large and diverse group of agenciesand individuals on urban, agricultural, range, and forested lands. It forces landand water managers to explore a realm of science that is little understood andoften poorly quantified. Hence, TMDLs can be defined as the maximum quantityof a pollutant that can enter a water body without adversely affecting thebeneficial uses of the water body;

TMDL = S WLAs + I LA + BL + MOS

where LA is Load Allocation which divides up and allocates the total quantity ofpollutant entering a water body daily among all the nonpoint sources; BL isBackground Loading that represents loads of naturally occurring materials thatwould have entered the water body prior to disturbance of the watershed byhuman activities, e.g., baseline phosphorus concentration derived from the naturalrocks in a watershed provides the native vegetation with their nutrient Prequirement; and MOS is Margin of Safety for the TMDL estimation.

Beyond the science, broad involvement in TMDLs also has sociologicalimplications. Because TMDLs address all the sources of pollution, fromindustrial effluent to fertilizer runoff from homeowner lawns, the TMDL processrequires delicate and dedicated attention to the science and sociology in eachwatershed. Every landowner and land user in a watershed is affected by TMDLs,


Management Information Systems 417

and broad awareness and involvement are very important. The magnitude ofWLA/TMDLs is a trend that will not only continue, but will likely increase.Once water quality standards for nutrients are established, the workload couldpotentially explode. In short, the challenges on every side of the equation arenumerous.

Water quality management is a critical component of the TMDL but itmust be intimately associated with an organized approach to best managementpractices (BMPs). Without BMPs, many of the nonpoint sources of pollutionwould remain unimpacted. However, approaches to BMP implementation needto be carefully evaluated and implemented to get the biggest benefit for theinvestment. Conservation districts, state and federal resource agencies willcontinue to play a crucial role in helping public and private land managers tocontinue to develop BMPs. On the urban side, municipal wastewater anddrinking water management agencies and urban developers will increasingly beinvolved in establishing and implementing BMPs for urban sources of nonpointsource pollution.

The general consensus is that there is a scarcity of data on many waterbodies that are suspected to be impaired. Furthermore, more data are needed toidentify sources of nonpoint source pollution within the watershed. NonpointSource Pollution management as well as BMP development is highly dependenton hydrologic simulation models due to its spatial and temporal extent in theproblem domain. Evaluating alternative management strategies throughexperiments and a limited amount of field measurements is not feasible, and amodeling study is often the only viable means of providing input to managementdecisions. (Yoon, 1998) The hydrologic system was commonly simplified inthe past as a "lumped model." Under this simplification, the spatial distributionof parameters lost their real meaning in hydrologic modeling. In contrast to thissimplification, a distributed parameter model maintains the spatial distribution ofthe parameters. Therefore, the application of distributed parameter models is ofpractical necessity especially in case of the nonpoint source pollutionmanagement.

The major disadvantage of distributed parameter models are the largeamounts of time required for assembling and manipulating the input data sets.The distributed nonpoint source pollution models used to study pollutanttransport and erosion easily generate towering amounts of data for analysis ineven a small watershed. Performing simulation and analysis for a large non-homogenous watershed would be very time consuming. Thus, the use of theGeographic Information System (GIS) would be advantageous, as it is ideallysuited to accommodate spatial variability over the temporal event horizon.Especially, integrating these spatiotemporally varying nonpoint source modelswith the GIS would be a viable alternative to improve the hydrologic modelingand analyzing NPS pollution.

This study focuses on establishing a "framework" for a watershed-scalewater quality management by establishing a bilateral linkage between an



engineering nonpoint source pollution water quality model (AGNPS) and a GIS.A Best Management Practice (BMP) generator was also conceptualized andseamlessly built inside the framework to accommodate evaluation of variousmanagement scenario alternatives to develop WLA/TMDLs.

The main focus in the GIS usage with existing models has been onedirectional data extraction to and from the model. Data handling was doneeither manually or by procedures that were created using specially writtensoftware. The large input data and parameter requirements, as well asdifficulties in analyzing the large amount of model results, can be a majorlimiting factor in the modeling process. Moreover in order to study theoverwhelming output estimates from the model, subgugate programs had to bewritten (Srinivasan and Engel, 1994). Although procedures were created toanalyze the output, these were loosely integrated with the model.

To obviate such difficulties, it is therefore necessary to develop datahandling procedures to prepare inputs to the model as well as to effect changesin the watersheds and procedures to analyze and display corresponding modelresults within the GIS environment, or a "framework". This essentiallyconstitutes the bilateral linkage approach. In order to make this integrationcomplete, a BMP generator was established inside the framework to generateoptimized BMP under a close-loop iterative scheme utilizing user-definedconstraints. This approach accommodates that after each simulation, the usercan evaluate whether there is a reduction in the amount of nutrients at the criticalareas inside the watershed by a BMP simulated, and can make further changes inBMP constraints if desired to improve the effectiveness of a BMP as a part ofWLA/TMDLs for a watershed-scale water resources management.

Study site

Naval Security Group Activity (NSGA) Northwest, is a Navy CommunicationsFacility on which the Navy, the Marines, the Coast Guard and the North AtlanticTreaty Organization (NATO) have facilities. The base occupies 1499 ha (3,700acres) of land, 70 percent of which is unimproved. The base straddles theVirginia-North Carolina border. Its main entrance is located on Ballahack roadin Chesapeake, Virginia. The NSGA Northwest leases approximately 303.75 ha(750 acres), primarily soybean and corn on the land. Stormwater discharge fromthe study site drains to the Northwest River, approximately 6.44 km (4 miles)upstream of the intake from the City of Chesapeake VA's potable watersupply(Gannet Fleming, 1995).

The topography of the watershed area mainly consists of woodlands,agricultural fields, pasture and urban areas. The crops grown are predominantlysoybean with corn used in rotation, 1-3 year crop rotation. The soil compositionof the study site consists of mainly coarse sandy soil susceptible to heavy erosionduring storm runoff overland flow. The depth of the topsoil is around 6 to 7inches. Most of the fields are flat at Northwest; all the fields are tilled as row



crops, with no contouring. The tillage practice consists of disk followed by fieldcultivator. The fertilizer application rate is 16.81 kg/ha (15 Ib/acre) nitrogen,33.62 kg/ha (30 Ib/acre) phosphorous. All site-specific data was assessed fromin-situ field survey.

Figure 1. Study Watershed: NSGA Northwest

Methodology

For this study, several digital spatial data layers were generated to provide inputfor AGNPS (Young et al., 1995). The integration process of data sources for themodel is illustrated in Table 1. The USGS Digital Elevation Model (DEM) inscale of 1:250,000 was used to derive watershed slope and aspect information.This was done by generating a lattice and then converting it to a polygoncoverage for slope and aspect determination. Aerial othophotographs (tiffformat, pseudocolor) were also used to estimate surface parameter aggregates.

Soil related coverages such as soil type, soil texture, soil erodabilityfactor (K), surface condition, Manning's coefficient was derived using NaturalResources Conservation Services (NRCS) Soil Survey and utilizing the aerialphotograph of the base. Cover and Practice factors that are based on landuse/cover, fertilization levels and SCS curve number were determined based onthe information given by in situ field surveys and the aerial photographinterpretation. Various other data layer which were either directly required asmodel input or as auxiliary parameters for model simulation, i.e., parameters thatwere not directly used for model input were generated. An Arc/INFO Grid was



created to generate the base map of the study area and then discretized into cellsusing a CAD software.

Table 1. Model input data types and methods of their determination

Data TypeSlope, Aspect, Channel SlopeSide Slope, Cell NumberSlope Length, Shape factorReceiving cellSoil Texture, Soil ErodabilityFactor (K)Practice Factor, Cover Factor,Fertilization Level, SCS curvenumber, Surface condition,Mannings coefficient

Data Source1 :25000Digital Elevation Model(DEM), USGS 1:24000 mapsSlope Length, Shape FactorAspect, Cell numberNRCS Soil Survey Book

Aerial Photograph, USDAAgriculture Handbook, AGNPSManual, Local Farmer

For efficient manipulation of spatial data for the model simulation viathe direct linkage, three core database schema were designed and implementedto facilitate a combined spatial database of the watershed as shown in Figure 3.The spatial database structures are categorized into three groups; (1) a non-alterable main database containing the static baseline data; (2) an alterabledatabase containing duplicates of the main database table for the scenariosimulations, and (3) an updateable AGNPS estimates database container.

For efficient manipulation of spatial data for the model simulation viathe direct linkage, three core database schema were designed and implementedto facilitate a combined spatial database of the watershed as shown in Figure 3.The spatial database structures are categorized into three groups; (1) a non-alterable main database containing the static baseline data; (2) an alterabledatabase containing duplicates of the main database table for the scenariosimulations, and (3) an updateable AGNPS estimates database container.After compilation of the database implementation, parameter attributes in thespatial database table were 'geolinked' or tagged to corresponding cells in theCIS coverages to establish the spatial relationship. Pre- and Post-processingmethods were then implemented within the framework to handle the input dataand output estimates of AGNPS back into the GIS environment to establish atrue bilateral linkage. To handle extensive input and output files during thelinkage, several Fortran programs were written. The joining of various spatialtables for model input format generation and the transfer of the output estimates,back to the spatial database was handled using AML (Arc Macro Language) inArc/INFO and ArcView for display and analysis, on a Sun Solaris UnixPlatform.



Figure 2. Descritization and parameterization of the study watershed.

Figure 3. Spatial database design and classification schema.

Summary and conclusions

A water quality modeling and management framework was implemented todevelop WLA/TMDL protocols through regulatory best management practices(BMPs) for a watershed scale nonpoint source problem in the NSGA Northwest,study site. Rather than manually managing simulation data from GIS database, abilateral linkage was implemented between the distributed parameter model andARC/INFO GIS, along with a built-in optimized, close loop iterative BMPgeneration process and an automated direct linkage results in an efficient, on-linemodeling and management decision-making framework capable ofinstantaneously visualizing the transport of the pollutants (nutrients) the modelcan simulate. Rather than limiting assessment to current watershed conditionoptimized BMPs can be generated within the framework for the various "what



if scenarios that the user may want to evaluate and optimize the predefined userobjective for management.

A case study was performed on a watershed outlet area at the NSGANorthwest using the implemented framework. Seven simulations based ondifferent landuse, management and hydrologic scenarios were performed to seetheir effects on pre-existing critical areas. Simulated results showed that theoptimal BMP scenario for WLA/TMDLs achieved a reduction of 41% in solubleand sediment attached nitrogen and about 62% reduction in soluble and sedimentphosphorous from their baseline concentrations (Figure 4). Also, a visualizationof improvement between before and after the optimized WLA/TMDL BMPscenario implementation are shown in Figures 5. The optimal BMP caused areduction in the nitrogen soluble concentration from 3.64 ppm to 1.64 ppm andthe phosphorous soluble concentration 1.25 ppm to 0.48 ppm. The averagenitrogen and phosphorous concentration for nutrient rich waters near urbanwatersheds is 1 mg/L for nitrogen and 0.1 mg/L for phosphorous (VDEQ WaterQuality Standards, 1997). Although reduction from the optimal BMP scenariodoes not confirm with the above standards they are relatively close. It should benoted that NSGA is a predominantly undeveloped rural watershed and thestandards are provided for drinking water in urban watersheds. Also the amountof reduction achieved in the nutrient concentrations would greatly reduce theload in the City of Chesapeake VA's drinking water supply unit.

References

Gannett Fleming, Inc., 1995, " Stormwater Pollution Prevention Plan," NavalSecurity Group Activity, Northwest, Chesapeake, Virginia, SummaryReport.

Srinivasan, R. and Engel, B. A., 1994, "A Spatial Decision Support System ForAssessing Agricultural Nonpoint Source Pollution," Water ResourcesBulletin, 30(3), pp. 441-452.

Tyler, E.L., 1992. Re-Authorization of the Clean Water Act The UniversitiesCouncil on Water Resources, Water Resources Update, Spring 1992, IssueNo. 88, pp. 7-15.

Yoon, J., 1998, "Watershed-Scale Nonpoint Source Pollution Managementbased on Spatiotempotal Distributed Parameter Model and GIS Linkage,"GIS technologies and their environmental applications, CMP, UK, pp. 3-12.

Young, R. A., Onstad, C. A., Bosch, D. D., and Anderson, W. P., 1995. AGNPSUser's Guide, Version 5.00, 1995, Agricultural Research Service, U.S.Department of Agriculture, Morris, MN.


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