| Title: | An integrated approach for modeling wetland water level: Application to a headwater wetland in coastal Alabama, USA |
| Authors: | Rezaeianzadeh, M., L. Kalin and M.M. Hantush |
| Year: | 2018 |
| Journal: | Water |
| Volume (Issue): | 10(7) |
| Pages: | |
| Article ID: | 879 |
| DOI: | 10.3390/w10070879 |
| URL (non-DOI journals): | |
| Model: | SWAT |
| Broad Application Category: | hydrologic only |
| Primary Application Category: | model and/or data interface |
| Secondary Application Category: | ENSO phenomena effects |
| Watershed Description: | 83.57 ha head water coastal wetland drainage area, located along the Gulf Coast in Baldwin County, Alabama, USA |
| Calibration Summary: | |
| Validation Summary: | |
| General Comments: | |
| 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. |
| Language: | English |
| Keywords: | |