An empirical method for approximating stream baseflow time series using groundwater table fluctuations

• Published in: Journal of hydrology (Amsterdam) Vol. 519; pp. 1031 - 1041
• Main Authors: Meshgi, Ali, Schmitter, Petra, Babovic, Vladan, Chui, Ting Fong May
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier B.V 27.11.2014; Elsevier
• Subjects: artificial intelligence; base flow; Baseflow; basins; Earth sciences; Earth, ocean, space; Empirical equation; empirical research; equations; Exact sciences and technology; Genetic Programming; groundwater; Hydrology. Hydrogeology; mathematical models; Numerical modeling; Singapore; soil; streams; time series analysis; United States; water table; watershed management; watersheds; Far East; Asia; Singapore; United States; Numerical modeling; Baseflow; Empirical equation; Genetic Programming; base flow; streams; ground water; vegetation; artificial intelligence; drainage basins; aquifers; fluctuations; management; numerical models; discharge; soils
• DOI: 10.1016/j.jhydrol.2014.08.033
• ISSN: 0022-1694

•Genetic programming is a successful tool for predicting baseflow.•Empirical equation predicts baseflow using groundwater table fluctuations.•The proposed equation performs as well as a recursive filter or a numerical model.•The generalized equation predicts baseflow irrespective of land use or scale. Developing reliable methods to estimate stream baseflow has been a subject of interest due to its importance in catchment response and sustainable watershed management. However, to date, in the absence of complex numerical models, baseflow is most commonly estimated using statistically derived empirical approaches that do not directly incorporate physically-meaningful information. On the other hand, Artificial Intelligence (AI) tools such as Genetic Programming (GP) offer unique capabilities to reduce the complexities of hydrological systems without losing relevant physical information. This study presents a simple-to-use empirical equation to estimate baseflow time series using GP so that minimal data is required and physical information is preserved. A groundwater numerical model was first adopted to simulate baseflow for a small semi-urban catchment (0.043km2) located in Singapore. GP was then used to derive an empirical equation relating baseflow time series to time series of groundwater table fluctuations, which are relatively easily measured and are physically related to baseflow generation. The equation was then generalized for approximating baseflow in other catchments and validated for a larger vegetation-dominated basin located in the US (24km2). Overall, this study used GP to propose a simple-to-use equation to predict baseflow time series based on only three parameters: minimum daily baseflow of the entire period, area of the catchment and groundwater table fluctuations. It serves as an alternative approach for baseflow estimation in un-gauged systems when only groundwater table and soil information is available, and is thus complementary to other methods that require discharge measurements.