Open Access Publications

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Open access publications by faculty, staff, postdocs, and graduate students from the Delaware Geological Survey.


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Now showing 1 - 4 of 4
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    Surface Water-Groundwater Connections as Pathways for Inland Salinization of Coastal Aquifers
    (Groundwater, 2022-11-17) Hingst, Mary C.; McQuiggan, Rachel W.; Peters, Chelsea N.; He, Changming; Andres, A. Scott; Michael, Holly A.
    Coastal agricultural zones are experiencing salinization due to accelerating rates of sea-level rise, causing reduction in crop yields and abandonment of farmland. Understanding mechanisms and drivers of this seawater intrusion (SWI) is key to mitigating its effects and predicting future vulnerability of groundwater resources to salinization. We implemented a monitoring network of pressure and specific conductivity (SC) sensors in wells and surface waters to target marsh-adjacent agricultural areas in greater Dover, Delaware. Recorded water levels and SC over a period of three years show that the mechanisms and timescales of SWI are controlled by local hydrology, geomorphology, and geology. Monitored wells did not indicate widespread salinization of deep groundwater in the surficial aquifer. However, monitored surface water bodies and shallow (<4 m deep) wells did show SC fluctuations due to tides and storm events, in one case leading to salinization of deeper (18 m deep) groundwater. Seasonal peaks in SC occurred during late summer months. Seasonal and interannual variation of SC was also influenced by relative sea level. The data collected in this study data highlight the mechanisms by which surface water-groundwater connections lead to salinization of aquifers inland, before SWI is detected in deeper groundwater nearer the coastline. Sharing of our data with stakeholders has led to the implementation of SWI mitigation efforts, illustrating the importance of strategic monitoring and stakeholder engagement to support coastal resilience.
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    Stormwater drives seasonal geochemical processes beneath an infiltration basin
    (Journal of Environmental Quality, 2022-10-11) McQuiggan, Rachel; Andres, Scott A.; Roros, Andreanna; Sturchio, Neil C.
    Deicing salt is an important component of road safety during winter storms. Stormwater infiltration best management practices aim to prevent the salt from polluting streams and waterways, but this may shift pollutants to groundwater resources. In response to limited field studies investigating groundwater quality impacts caused by input of salt from stormwater infiltration best management practices, we monitored water levels and quality of groundwater at various depths in an unconfined aquifer around a stormwater infiltration basin using in situ sensors coupled with grab sampling. Our observations revealed differences in groundwater chemistry with depth in the aquifer and processes that were driven by the seasonal changes in the chemistry of stormwater (salt-impacted in winter and fresh in non-winter) recharging the aquifer. Water–matrix interactions in the vadose zone beneath the basin affected the transport of sodium (Na) into groundwater following non-winter recharge. Sodium movement through the aquifer was delayed relative to chloride (Cl), indicating a longer residence time of Na in the vadose zone. Radium (Ra) concentrations were correlated with Cl concentrations, suggesting salt-impacted recharge caused desorption of Ra into groundwater because of increased salinity. Stormwater-influenced groundwater followed a preferential flow path due to heterogeneity of the aquifer materials, and water chemistry varied with time and location along the flow path. These results highlight the importance of well screen length, placement and depth, and frequency of observations when designing a monitoring network.
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    Downscaling satellite soil moisture for landscape applications: A case study in Delaware, USA
    (Journal of Hydrology: Regional Studies, 2021-10-15) Warner, Daniel L.; Guevara, Mario; Callahan, John; Vargas, Rodrigo
    Study region: Delaware, USA and its surrounding watersheds. Study focus: An ensemble using multiple Kernel K-nearest neighbors (KKNN) models was trained to predict daily grids of SSM at 100-meter resolution based on SSM estimates from the European Space Agency’s Climate Change Initiative Soil Moisture Product, terrain data, soil maps, and local meteorological network data. Estimated SSM was evaluated against independent in situ SSM observations and were investigated for relationships with land cover class and vegetation phenology (i.e., NDVI). New hydrological insights for the region Downscaled daily mean SSM estimates had lower error in space (27%) and greater predictive performance over time compared to the raw, coarse resolution remotely sensed SSM dataset when calibrated to field observed values. Downscaled SSM identified stronger and more widespread temporal relationships with NDVI than other estimation methods. However, both coarse and fine resolution datasets greatly underestimated SSM in wetland areas. The findings highlight the need for enhanced in situ SSM monitoring across diverse settings to improve landscape-level downscaled SSM. The downscaling methodology developed in this study was able to produce daily SSM estimates, providing a framework that can support future SSM modeling efforts, hydroecological investigations, and agricultural studies in this and other regions around the world when used in conjunction with ground-based monitoring networks.
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    Spatial biases of information influence global estimates of soil respiration: How can we improve global predictions?
    (Global Change Biology, 2021-05-01) Stell, Emma; Warner, Daniel; Jian, Jinshi; Bond-Lamberty, Ben; Vargas, Rodrigo
    Soil respiration (Rs), the efflux of CO2 from soils to the atmosphere, is a major component of the terrestrial carbon cycle, but is poorly constrained from regional to global scales. The global soil respiration database (SRDB) is a compilation of in situ Rs observations from around the globe that has been consistently updated with new measurements over the past decade. It is unclear whether the addition of data to new versions has produced better-constrained global Rs estimates. We compared two versions of the SRDB (v3.0 n = 5173 and v5.0 n = 10,366) to determine how additional data influenced global Rs annual sum, spatial patterns and associated uncertainty (1 km spatial resolution) using a machine learning approach. A quantile regression forest model parameterized using SRDBv3 yielded a global Rs sum of 88.6 Pg C year−1, and associated uncertainty of 29.9 (mean absolute error) and 57.9 (standard deviation) Pg C year−1, whereas parameterization using SRDBv5 yielded 96.5 Pg C year−1 and associated uncertainty of 30.2 (mean average error) and 73.4 (standard deviation) Pg C year−1. Empirically estimated global heterotrophic respiration (Rh) from v3 and v5 were 49.9–50.2 (mean 50.1) and 53.3–53.5 (mean 53.4) Pg C year−1, respectively. SRDBv5’s inclusion of new data from underrepresented regions (e.g., Asia, Africa, South America) resulted in overall higher model uncertainty. The largest differences between models parameterized with different SRDVB versions were in arid/semi-arid regions. The SRDBv5 is still biased toward northern latitudes and temperate zones, so we tested an optimized global distribution of Rs measurements, which resulted in a global sum of 96.4 ± 21.4 Pg C year−1 with an overall lower model uncertainty. These results support current global estimates of Rs but highlight spatial biases that influence model parameterization and interpretation and provide insights for design of environmental networks to improve global-scale Rs estimates.
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