Ice cover exists: A quick method to delineate groundwater inputs in running waters for cold and temperate regions

Abstract

Groundwater can be important in regulating stream thermal regimes in cold, temperate regions, and as such, it can be a significant factor for aquatic biota habits and habitats. Groundwater typically remains at a constant temperature through time; that is, it is warmer than surface water in winter and cooler in summer. Further, small tributaries are often dominated by groundwater during low flows of winter and summer.We exploit these thermal patterns to identify and delineate tributary/groundwater inputs along a frozen river (ice-on) using publically available satellite data, and we tested the findings against airborne, thermal infrared (TIR) data. We utilize a supervised maximum likelihood classification (sMLC) to identify possible groundwater inputs while the river is in a frozen state (kappa coefficient of 96.77 when compared with visually delineated possible groundwater inputs). We then compare sMLCidentified possible groundwater inputs with TIR-classified groundwater inputs, which confirmed that there was no statistical difference (χ 2 = .78), that is, confirming that groundwater inputs can be delineated in north temperate river systems using available satellite imagery of the system's frozen state. Our results also established the spatial extent and influence of possible groundwater inputs in two seasons. The thermal plumes were longer and narrower in winter; this is likely related to seasonal differences in dispersion regimes. We hypothesize that differences between summer and winter is related to either (a) tributaries that are modulated by shading in summer or (b) aquifer disconnection from the river in winter owing to frozen ground conditions and lack of aquifer recharge. This method of establishing tributary/groundwater inputs and contributions to surface water thermal regimes is relatively simple and can be useful for science and management as long as "ice cover exists"; that is, the system can achieve a frozen state. K E Y W O R D Sgroundwater, ice, maximum likelihood classification, satellite imagery, summer/winter, thermal dispersion, thermal infrared, thermal refugia

Characterizing physical habitat preferences and thermal refuge occupancy of brook trout (Salvelinus fontinalis) and Atlantic salmon (Salmo salar) at high river temperatures

“…There was no deviation of anomaly locations between 2008, 2009, and the field observations of 2010 (Wilbur, ). This is consistent with other studies that found thermal anomalies in the Cains River were spatially and temporally stable between 2008/2009 and 2014 (see O'Sullivan, Linnansaari, & Curry, ). The river's groundwater inputs are derived from intermediate to long, groundwater flow paths, are less susceptible to fluxes in contemporaneous climate, and therefore are spatially and temporally stable.…”

Effects of Topographic Resolution and Geologic Setting on Spatial Statistical River Temperature Models

“…In this study, we found mixed success and sometimes poor outcomes for the SSN models, and the reason appears to be a function of geology. Emerging from our current and past studies (Monk et al, 2013; O'Sullivan, Linnansaari, et al, 2019) and relatively poor SSN performance in regions with similarly variable geology and topography (e.g., Detenbeck et al, 2016—New England, northeastern United States) is a working hypothesis that SSNs are effective in regions where the relationship between climate, groundwater, and topography is regionally constant, that is, unidirectional or bidirectional (Gleeson & Manning, 2008; Tóth, 1962).…”

“…The influence of geology on river flow and temperature at the catchment‐scale has been largely neglected in process and modeling studies, but geology is emerging as a key hydrological control (e.g., Briggs et al, 2017; Carlier et al, 2019; O'Sullivan, Linnansaari, et al, 2019; Tague & Grant, 2004). Current SSN models have great potential, but their applicability for hydrological process analysis is likely limited to areas with constant groundwater/topography regimes.…”

“…However, similar to the Cains Rivers, the North Pole Stream SRTM DEM had a positive bias for vegetated areas, and a negative bias when measuring over waterbodies data (see Figure S4), suggesting LiDAR better represents topographic structure, regardless. In the North Pole Stream steep and confined, low conductance, shallow bedrock valleys do not produce groundwater inflows and river temperatures can be warm from heat gains by solar radiation (Caissie, 2006a, Figures 7 and 8), shallow groundwater heating (Briggs et al, 2017), and fluid friction (O'Sullivan, Linnansaari, & Curry, 2019). We suggest that LiDAR has limited ability to improve river temperature model performance in areas defined by opposing groundwater and topography interactions (Gleeson et al, 2011), unless the models also include parameters that address subsurface processes (Larocque & Broda, 2016).…”

Development of Active Numerating Side-scan for a High-Density Overwintering Location for Endemic Shortnose Sturgeon (Acipenser brevirostrum) in the Saint John River, New Brunswick

“…We used a well-established supervised maximum likelihood classification (sMLC) machine learning algorithm to classify the objects in the sonar images described above. sMLC is based on Bayesian probability theory [37] and requires an initial training data suite to define the classes of interest. In this study, those classes were (1) potential Shortnose Sturgeon, and (2) river bed.…”