Watgis: A Gis–based Lumped Parameter Water Quality Model

Abstract

A Geographic Information System (GIS)-E valuation of the cumulative effects of land use and water management practices on downstream hydrology and water quality of a watershed requires consideration of many factors including climate, geology, soils, vegetation cover, and soil and water management practices. The interaction of these factors is complex; computer simulation models provide a means of integrating contributions from each of them. The spectrum of models used for water quality planning and assessment range from the more comprehensive process-based models such as WASP4 (Ambrose et al., 1981), QUAL2E (EPA, 1987, HSPF (Johanson et al., 1981), DUFLOW (Aalderlink et al., 1995), and CE-QUAL-RIVl (Dortch et al., 1990) to the conceptual and/or highly simplified lumped parameter models (Haith and Shoemaker, 1987;Reckhow et al., 1992;Johnes, 1996). Complex models are capable of simulating the impacts of the dynamics of natural processes in large watersheds on a shorter time scale. However, as decision tools for planners, they are difficult to use due to their high input data requirements, problems in calibration in large watersheds, and parameterization. In addition, the underlying uncertainties in the formulation of processes and parameterization often contribute to uncertainties in predictions (Beck, 1987). In most cases, the choice of a model depends upon the user's knowledge of the model, data availability, the system where it is applied, and the desired application. Most decision makers working with watershed-level management may only need planning-level information. This cou!d be obtained easily from the use of lumped parameter models, which require minimal input data to run and are capable of accurate predictions on longer time scales (Cooper and Bottcher, 1993). When coupled with error and uncertainty analyses, lumped parameter models can provide decision makers with more information than the traditional deterministic output. Moreover, the time and effort needed to run these models are considerably less than what is required in using physically based models. Through research conducted on the coastal plain soils over the years (Skaggs and Gilliam, 1981;Gilliam and Skaggs, 1986;Amatya et al., 1998), the magnitude of nutrient losses (N and P) from agricultural and forested lands and the factors affecting these losses have been determined under various water management (e.g., controlled drainage) and nutrient management practices. Recent studies by Birgand (2000) have characterized the in-stream processes occurring in agricultural ditches and canals of lower coastal plain Vol. 45(3): 593-m

Water Quality Monitoring and Cadmium Contamination in the Sediments of Mae Tao Stream, Mae Sot District, Tak Province, Thailand

“…The major ions that contribute to the measurement of conductivity are sodium, potassium, calcium, magnesium, chloride, sulfate ions. Other ions that contribute to conductivity to a smaller degree are carbonate, bicarbonate, nitrate and phosphate [10]. In this study, EC values ranged from 365 to 402 μS cm -1 (Table I) and at all the sampling sites EC were very high.…”

Mouthpart deformities in non-biting midge larvae from a cadmium contaminated stream in Northern Thailand

“…The major ions contributing to the conductivity are sodium, potassium, calcium, magnesium, chloride, and sulphate. Other ions that contribute to conductivity to a smaller degree are carbonate, bicarbonate, nitrate, and phosphate 15 . In this study, EC values ranged from 365-402 µS/cm (Table 1) and were high at all sampling sites.…”

Impact of Seasonality on Artificial Drainage Discharge under Temperate Climate Conditions

“…The lower proportion of discharge for sandy soils contrasts with the results of common water seepage models, which calculate a higher seepage rate for sandy soils than for finer structured soils (loamy, silty and clayey soils). Higher seepage rate for sandy soils are also calculated by using models such as DRAINMOD (AMATYA et al, 2004;FERNANDEZ et al, 2002;SKAGGS, 1978). The artificial drainage discharge data covering study sites with different soil textural classes that we now present, diverges from Darcy's law assuming homogeneous flow conditions, in that the sites start to drain rapidly after precipitation events.…”