RESUMO
The upper Mississippi River basin has been identified as the most significant contributor of excessive nutrients to the hypoxic zone in the Gulf of Mexico. The land-use changes from an internally drained prairie-wetland complex to an intensively managed corn-soybean production system drained by subsurface tile drainage system in the north-central Iowa and south-central Minnesota are the primary cause of nutrient loads into the Mississippi River and many other environmental stresses. The present study summarizes the water-quality degradation from land-use change and offers a fuzzy logic-based decision support for assessing degree of suitability of the four recommended perennial plant options for managing water and nitrate-nitrogen export. These options are designed based on landscape position that currently fails to produce high yielding row crops and scale: (1) marginal upland depressions for water storage by planting deep-rooted perennial grasses and fast-growing woody poplar, willow, and alder in poorly drained swales; (2) saturated buffers and/or subtle changes in landscape slope for draining high nitrate-nitrogen subsurface (through multi-species phytoremediation treatment buffers or strips of perennial vegetation); (3) two-stage ditches with linear floodplains planted with perennial grasses; and (4) riparian and in-channel ecologically engineered trees, shrubs, and grasses to better connect meander belt width to frequent peak stream flows at larger scales. When applied throughout a typical (Des Moines Lobe Till) DMLT watershed, each option can have positive cumulative environmental effects. Fuzzy logic enhanced the precision in watershed decision-making by incorporating the uncertainty associated with factors like cost effectiveness, nitrate reduction potential, water quality improvement, and level of acceptance.
Assuntos
Nitratos , Rios , Agricultura , Monitoramento Ambiental , Nitratos/análise , Nitrogênio/análise , Óxidos de Nitrogênio , IncertezaRESUMO
Assessment of water quality status of a river with respect to its discharge has become prerequisite to sustainable river basin management. The present paper develops an integrated model for simulating and evaluating strategies for water quality management in a river basin management by controlling point source pollutant loadings and operations of multi-purpose projects. Water Quality Analysis and Simulation Program (WASP version 8.0) has been used for modeling the transport of pollutant loadings and their impact on water quality in the river. The study presents a novel approach of integrating fuzzy set theory with an "advanced eutrophication" model to simulate the transmission and distribution of several interrelated water quality variables and their bio-physiochemical processes in an effective manner in the Ganges river basin, India. After calibration, simulated values are compared with the observed values to validate the model's robustness. Fuzzy technique of order preference by similarity to ideal solution (F-TOPSIS) has been used to incorporate the uncertainty associated with the water quality simulation results. The model also simulates five different scenarios for pollution reduction, to determine the maximum pollutant loadings during monsoon and dry periods. The final results clearly indicate how modeled reduction in the rate of wastewater discharge has reduced impacts of pollutants in the downstream. Scenarios suggesting a river discharge rate of 1500 m3/s during the lean period, in addition to 25 and 50% reduction in the load rate, are found to be the most effective option to restore quality of river Ganges. Thus, the model serves as an important hydrologic tool to the policy makers by suggesting appropriate remediation action plans.