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.

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.

Avocado ( Persea americana Mill.) production and consumption are rapidly increasing worldwide, with the global yield expected to reach 12 million tons by 2030.

Groundwater discharge plays an important role in the hydrologic and ecologic functioning of rivers including sustaining streamflow and related habitat year‐round. Simultaneously, groundwater supports the increasing demands of people as the global population continues to grow. Balancing the needs of users becomes increasingly important as climate change introduces greater uncertainty in water resources and fisheries, especially for economically important anadromous species that depend on freshwater resources. We investigated the seasonal and regional variability of groundwater contributions to six non‐glacial mainstem salmon‐bearing rivers in south‐central Alaska. We hypothesized that groundwater contributes more than half of the annual streamflow, and nearly all of the streamflow during late summer and mid‐winter periods. Our results show that groundwater discharge is the dominant source of streamflow, with an annual relative contribution of 70%. Groundwater contribution to streamflow varies seasonally, ranging from approximately 50%–70% during peak flow to 70%–80% during low flow. Groundwater contribution to streamflow also varies spatially, with maximum differences in groundwater contribution between watersheds ranging ±20 percentage points. However, all rivers in the study area followed similar trends, with higher groundwater contributions in summer and winter and lower contributions in spring and fall.

Land use-land cover (LULC) change is widely implicated in coastal water quality degradation, but we often lack understanding of how LULC change has varied spatiotemporally because so much change occurred before the advent of modern mapping. In this study, we overcome this challenge by using forensic mapping techniques of LULC change over the past 175 years in coastal watersheds on the Atlantic Coast of Florida, USA. We benchmark historical mapping products to modern mapping standards using historical products. These include maps and notes from the Public Land Survey System, military campaigns, and navigation surveys from the nineteenth century, and aerial imagery, soil surveys, topographic maps, and LULC maps from the 20th and 21st centuries. Through this mixed approach, we mapped wetlands and lakes, as well as natural and constructed channels in the 1850s, 1950s, 2000s, and 2020s. Results illustrate the stunning transformation of these coastal watersheds over the past 175 years, including an 89% loss of wetlands and lakes and a 25,000% increase in drainage density, initially mostly due to agricultural conversion. These changes have greatly increased the hydrological connectivity between inland landscapes and the adjacent estuaries. Interestingly, these changes occurred over different intervals, with most wetland and lake loss occurring between the 1950s–2000s but most of the increase in drainage density occurring earlier between the 1850s–1950s. These more nuanced and spatially explicit understandings of LULC change are facilitating efforts to restore wetlands and drainage networks, including ongoing stakeholder efforts to plan land acquisition for conservation and water-quality restoration.