A Review of Recent Water Quality Assessments in Watersheds of Southeastern United States using Continuous Time Models

Water quality is an indicator of the health and safety of the environment which is subjected to natural and human-induced changes. Although a considerable amount of research has been done to explain the different factors affecting water quality and the various approaches to predict spatiotemporal water quality factors, there are several challenges in the water quality modeling scenarios. The article revisits the water quality assessments conducted in the watersheds of southeastern United States using continuous time models with the aid of statistical analysis, remote sensing, theoretical models, and bigdata based approaches. The review illuminates that the need of the hour is the inclusion of novel prediction methods, quick access to updated and right water quality data, arriving at usable factors to improve prediction reliability, exploring combined conceptual methods of analysis, and collecting more field reference data that can benchmark the developed water quality models.

Keywords:Water quality; Continuous models; Remote sensing; Southeastern United States; Nutrients; Climate change; Machine learning

Abbreviations:GIS - Geographical Information System; SWIM - Surface Water Improvement and Management; SWAT - Soil and Water Assessment Tool; MIKE SHE - Integrated Hydrological Modelling System; IDW - inverse distance weight; GRACE - Gravity Recovery and Climate Experiment; MODIS – Moderate-resolution Imaging Spectroradiometer