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Groundwater extraction compromises the function of groundwater-dependent ecosystems, such as freshwater wetlands. Identifying whether groundwater conservation restores wetland hydrology is a first step toward rehabilitating impaired wetlands. In the Tampa Bay region of Florida (U.S.), groundwater extraction rates have been declining since 1998, partly in response to desiccation of wetlands and waterbodies. This study uses monthly water-level data from 152 depressional wetlands over 28 years (1991–2018) to identify trends in wetland inundation, determine whether those trends vary among wetlands historically exposed to different rates of groundwater extraction, and describe relationships between the timing and extent of cutbacks in groundwater extraction and the timing and extent of changes in wetland inundation. Many wetlands (57 %) exhibited increased inundation in response to cutbacks in groundwater extraction, indicating that water conservation measures are inducing recovery. Further, increased inundation began in most wetlands immediately upon, or within two years of, the time extraction cutbacks occurred, although some recovering wetlands exhibited longer lags. An additional 26 % of wetlands had steady-state water levels with inundation similar to that of reference wetlands, potentially revealing a population of wetlands hydrologically unimpaired by nearby groundwater extraction. Another subset of wetlands (14 %) with steady-state water depths exhibited increasing deviations from basin-full water levels, suggesting subsidence of the wetland basin. Active intervention beyond cutbacks in groundwater extraction may be necessary to restore this subset, whereas passive restoration (reducing extraction) appears adequate for the majority of impacted wetlands. Rising water levels may amplify surface-water connections among wetlands, with ecological and biogeochemical consequences both for individual wetlands and for the whole wetlandscape. As a host of human activities continue to rely on groundwater extraction, this study demonstrates the potential for, as well as variability in, hydrological recovery across a wetland-rich, low-relief landscape following the enactment of water conservation policies. 

Wetlands provide essential ecosystem services, including nutrient cycling, flood protection, and biodiversity support, that are sensitive to changes in wetland hydrology. Wetland hydrological inputs come from precipitation, groundwater discharge, and surface run-off. Changes to these inputs via climate variation, groundwater extraction, and land development may alter the timing and magnitude of wetland inundation. Here, we use a long-term (14-year) comparative study of 152 depressional wetlands in west-central Florida to identify sources of variation in wetland inundation during two key time periods, 2005–2009 and 2010–2018. These time periods are separated by the enactment of water conservation policies in 2009, which included regional reductions in groundwater extraction. We investigated the response of wetland inundation to the interactive effects of precipitation, groundwater extraction, surrounding land development, basin geomorphology, and wetland vegetation class. Results show that water levels were lower and hydroperiods were shorter in wetlands of all vegetation classes during the first (2005–2009) time period, which corresponded with low rainfall conditions and high rates of groundwater extraction. Under water conservation policies enacted in the second (2010–2018) time period, median wetland water depths increased 1.35 m and median hydroperiods increased from 46 % to 83 %. Water-level variation was additionally less sensitive to groundwater extraction. The increase in inundation differed among vegetation classes with some wetlands not displaying signs of hydrological recovery. After accounting for effects of several explanatory factors, inundation still varied considerably among wetlands, suggesting a diversity of hydrological regimes, and thus ecological function, among individual wetlands across the landscape. Policies seeking to balance human water demand with the preservation of depressional wetlands would benefit by recognizing the heightened sensitivity of wetland inundation to groundwater extraction during periods of low precipitation. 

Understanding where groundwater recharge occurs is essential for managing groundwater resources, especially source-water protection. This can be especially difficult in remote mountainous landscapes where access and data availability are limited. We developed a groundwater recharge potential (GWRP) map across such a landscape based on six readily available datasets selected through the literature review: precipitation, geology, soil texture, slope, drainage density, and land cover. We used field observations, community knowledge, and the Analytical Hierarchy Process to rank and weight the spatial datasets within the GWRP model. We found that GWRP is the highest where precipitation is relatively high, geologic deposits are coarse-grained and unconsolidated, soils are variants of sands and gravels, the terrain is flat, drainage density is low, and land cover is undeveloped. We used GIS to create a map of GWRP, determining that over 83% of this region has a moderate or greater capacity for groundwater recharge. We used two methods to validate this map and assessed it as approximately 87% accurate. This study provides an important tool to support informed groundwater management decisions in this and other similar remote mountainous landscapes. 

We hypothesized topographic features alone could be used to locate groundwater discharge, but only where diagnostic topographic signatures could first be identified through the use of limited field observations and geologic data. We built a geodatabase from geologic and topographic data, with the geologic data only covering ~40% of the study area and topographic data derived from airborne LiDAR covering the entire study area. We identified two types of groundwater discharge: shallow hillslope groundwater discharge, commonly manifested as diffuse seeps, and aquifer-outcrop groundwater discharge, commonly manifested as springs. We developed multistep manual procedures that allowed us to accurately predict the locations of both types of groundwater discharge in 93% of cases, though only where geologic data were available. However, field verification suggested that both types of groundwater discharge could be identified by specific combinations of topographic variables alone. We then applied maximum entropy modeling, a machine learning technique, to predict the prevalence of both types of groundwater discharge using six topographic variables: profile curvature range, with a permutation importance of 43.2%, followed by distance to flowlines, elevation, topographic roughness index, flow-weighted slope, and planform curvature, with permutation importance of 20.8%, 18.5%, 15.2%, 1.8%, and 0.5%, respectively. The AUC values for the model were 0.95 for training data and 0.91 for testing data, indicating outstanding model performance.