An Integrated Approach for Modeling Wetland Water Level: Application to a Headwater Wetland in Coastal Alabama, USA

Abstract

Headwater wetlands provide many benefits such as water quality improvement, water storage, and providing habitat. These wetlands are characterized by water levels near the surface and respond rapidly to rainfall events. Driven by both groundwater and surface water inputs, water levels (WLs) can be above or below the ground at any given time depending on the season and climatic conditions. Therefore, WL predictions in headwater wetlands is a complex problem. In this study a hybrid modeling approach was developed for improved WL predictions in wetlands, by coupling a watershed model with artificial neural networks (ANNs). In this approach, baseflow and stormflow estimates from the watershed draining to a wetland are first estimated using an uncalibrated Soil and Water Assessment Tool (SWAT). These estimates are then combined with meteorological variables and are utilized as inputs to an ANN model for predicting daily WLs in wetlands. The hybrid model was used to successfully predict WLs in a headwater wetland in coastal Alabama, USA. The model was then used to predict the WLs at the study wetland from 1951 to 2005 to explore the possible teleconnections between the El Niño Southern Oscillation (ENSO) and WLs. Results show that both precipitation and the variations in WLs are partially affected by ENSO in the study area. A correlation analysis between seasonal precipitation and the Nino 3.4 Index suggests that winters are wetter during El Niño in Coastal Alabama. Analysis also revealed a significant negative correlation between WLs and the Nino 3.4 Index during the El Niño phase for spring. The findings of this study and the developed methodology/tools are useful to predict long-term WLs in wetlands and construct more accurate restoration plans under a variable climate.

Watershed Modelling of the Mindanao River Basin in the Philippines Using the SWAT for Water Resource Management

“…The SWAT application is very challenging in a largescale model and in wetlands. The wetlands normally absorb the surface and subsurface water between the inlet and outlet at several points, even if the inlet is well-defined [52]. Although SWAT already employs the basic concept of wetlands (marshlands), its ability to emulate the riparian wetland-river interaction is still under-studied [53].…”

The prediction of water level based on support vector machine under construction condition of steel sheet pile cofferdam

“…Besides, with the continuous progress of computer technology for big data processing, all factors affecting water level fluctuation can be considered. Artificial neural networks have the advantage of predicting groundwater level, 36 river level, 37‐39 reservoir level, 40‐42 and wetland level 43,44 …”

Development of Water Level Prediction Models Using Machine Learning in Wetlands: A Case Study of Upo Wetland in South Korea

“…In recent times, it has become possible to take into account all the factors affecting water level fluctuation, due to the development of computer technology for processing big data. ANN is widely used to forecast the groundwater level [32], water level in rivers [33][34][35], reservoirs [36][37][38], and wetlands [39,40]. However, research into water level prediction in wetlands is more recent than other areas of study, with the research focusing on the use of ANN [39,40].…”

“…ANN is widely used to forecast the groundwater level [32], water level in rivers [33][34][35], reservoirs [36][37][38], and wetlands [39,40]. However, research into water level prediction in wetlands is more recent than other areas of study, with the research focusing on the use of ANN [39,40]. Unlike studies on water level prediction in rivers, reservoirs, and groundwater, where ML is applied to evaluate and compare local ML models, studies using ANN alone could not be used to compare and evaluate the applicability to various ML models.…”