Continuous and event-based time series analysis of observed floodplain groundwater flow under contrasting land-use types

2017

Ecohydrology

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Two floodplain sites were instrumented, a bottomland hardwood forest and a historic agricultural field. Continuous, automated, in situ shallow groundwater monitoring was paired with a gridded sampling design to facilitate detailed spatiotemporal analysis of water table response to precipitation events. Data were collected at 30‐min intervals for the duration of the 2011, 2012, 2013, and 2014 water years. Four hundred three precipitation events were observed, with median depth of 3 mm. Results show significantly (p < .001) greater study site median water table response at the agricultural site (0.02 m), relative to the forest (0.01 m). The ratio of water table response magnitude to precipitation depth was 8.3 and 3.3 at the agricultural and forest sites, respectively, a difference of more than 150%. Median shallow groundwater level lag time was shorter with respect to both precipitation and streamflow at the agricultural site (10 and 2 hr for precipitation and flow, respectively), relative to the forest (11.5 and 4.5 hr for precipitation and flow, respectively). Longer lag times indicated slower water table response and potentially increased flood attenuation capacity by the forest site. Results show the agricultural site water table responded to a greater number of precipitation events (including smaller events), reflecting potentially reduced relative flood attenuation capacity. Observed differences highlight the various impacts of forest vegetation on water table dynamics and emphasize the benefits of floodplain forests as a water resource management tool, including for flood mitigation, in mixed‐land‐use watersheds.

Section: Discussionmentioning

confidence: 78%

Section: Discussionmentioning

confidence: 78%

Land use impacts on floodplain water table response to precipitation events

2017

Ecohydrology

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“…where r is the radius of the well casing (cm), R is the radius of the well screen (cm), L e is the length of the well screen (cm), and t 37 (s) is the time it takes for the water level inside the piezometer to rise or fall 37% of the initial change during a slug test. It should be noted that the average K sat derived from slug tests requires the assumption that the average K sat is representative of the actual field conditions [23].…”

Section: Field Saturated Hydraulic Conductivitymentioning

confidence: 99%

“…Additionally, field-based K sat estimates can be limited by incomplete aquifer geometry information, while laboratory methods can present problems with obtaining representative sample numbers. These challenges suggest the need for methods to estimate K sat that are accurate and efficient [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

A Comparison and Validation of Saturated Hydraulic Conductivity Models

Gootman

2020

Water

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Saturated hydraulic conductivity (Ksat) is fundamental to shallow groundwater processes. There is an ongoing need for observed and model validated Ksat values. A study was initiated in a representative catchment of the Chesapeake Bay Watershed in the Northeast USA, to collect observed Ksat and validate five Ksat pedotransfer functions. Soil physical characteristics were quantified for dry bulk density (bdry), porosity, and soil texture, while Ksat was quantified using piezometric slug tests. Average bdry and porosity ranged from 1.03 to 1.30 g/cm3 and 0.51 to 0.61, respectively. Surface soil (0–5 cm) bdry and porosity were significantly (p < 0.05) lower and higher, respectively, than deeper soils (i.e., 25–30 cm; 45–50 cm). bdry and porosity were significantly different with location (p < 0.05). Average soil composition was 92% sand. Average Ksat ranged from 0.29 to 4.76 m/day and significantly differed (p < 0.05) by location. Four models showed that spatial variability in farm-scale Ksat estimates was small (CV < 0.5) and one model performed better when Ksat was 1.5 to 2.5 m/day. The two-parameter model that relied on silt/clay fractions performed best (ME = 0.78 m/day; SSE = 20.68 m2/day2; RMSE = 1.36 m/day). Results validate the use of simple, soil-property-based models to predict Ksat, thereby increasing model applicability and transferability.

“…Such alterations impact water quality (e.g., chemical composition, pathogen presence and persistence) and quantity regimes (e.g., low flows, peak flows, flooding) [5][6][7][8], and can ultimately result in ecosystem degradation. However, water quality and quantity regimes are variably impacted by natural and anthropogenic factors, and are consequently difficult to quantify and manage in contemporary watersheds [6,9], where a mosaic of land use types confounds attributing causative mechanisms to observed alterations of hydrologic regimes.…”

Section: Introductionmentioning

confidence: 99%

Advancing Understanding of the Surface Water Quality Regime of Contemporary Mixed-Land-Use Watersheds: An Application of the Experimental Watershed Method

2017

Hydrology

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A representative watershed was instrumented with five gauging sites (n = 5), partitioning the catchment into five nested-scale sub-watersheds. Four physiochemical variables were monitored: water temperature, pH, total dissolved solids (TDS), and dissolved oxygen (DO). Data were collected four days per week from October 2010-May 2014 at each gauging site. Statistical analyses indicated significant differences (p < 0.05) between nearly every monitoring site pairing for each physiochemical variable. The water temperature regime displayed a threshold/step-change condition, with an upshifted and more variable regime attributable to the impacts of urban land uses. TDS, pH, and DO displayed similar spatiotemporal trends, with increasing median concentrations from site #1 (agriculture) to #3 (mixed-use urban) and decreasing median concentrations from site #3 to #5 (suburban). Decreasing concentrations and increasing streamflow volume with stream distance, suggest the contribution of dilution processes to the physiochemical regime of the creek below urban site #3. DO concentrations exceeded water quality standards on an average of 31% of observation days. Results showed seasonal trends for each physiochemical parameter, with higher TDS, pH, and DO during the cold season (November-April) relative to the warm season (May-October). Multivariate modeling results emphasize the importance of the pH/DO relationship in these systems, and demonstrate the potential utility of a simple two factor model (water temperature and pH) in accurately predicting DO. Collectively, results highlight the interacting influences of natural (autotrophic photosynthesis, organic detritus loading) and anthropogenic (road salt application) factors on the physiochemical regime of mixed-land-use watersheds.