Untangling complex shallow groundwater dynamics in the floodplain wetlands of a southeastern U.S. coastal river

Abstract

[1] Understanding the hydrological functioning of tidally influenced floodplain forests is essential for advancing ecosystem protection and restoration goals in impacted systems. However, finding direct relationships between basic hydrological inputs and floodplain hydrology is hindered by complex interactions between surface water, groundwater, and atmospheric fluxes in a variably saturated matrix with heterogeneous soils, vegetation, and topography. Thus, an explanatory method for identifying common trends and causal factors is required. Dynamic factor analysis (DFA), a time series dimension reduction technique, models temporal variation in observed data as linear combinations of common trends, which represent unidentified common factors, and explanatory variables. In this work, DFA was applied to model water table elevation (WTE) in the floodplain of the Loxahatchee River (Florida, USA), where altered watershed hydrology has led to changing hydroperiod and salinity regimes and undesired vegetative changes in the floodplain forest. The technique proved to be a powerful tool for the study of interactions among 29 long-term, nonstationary hydrological time series (12 WTE series and 17 candidate explanatory variables). Regional groundwater circulation, surface water elevations, and spatially variable net local recharge (cumulative rainfall -cumulative evapotranspiration) were found to be the main factors explaining groundwater profiles. The relative importance of these factors was spatially related to floodplain elevation, distance from the river channel, and distance upstream from the river mouth. The resulting dynamic factor model (DFM) simulated the WTE time series well (overall coefficient of efficiency, C eff = 0.91) and is useful for assessing management scenarios for ecosystem restoration and predicted sea level rise.

Hydrological controls of fisheries production in a major Amazonian tributary

“…Initially developed for analysis of economic models (Engle & Watson, ; Geweke, ), DFA has more recently been applied to better understand a variety of physical and biological processes, from commercial fisheries production (Begoña Santos et al, ; Erzini et al, ; Pérez‐Rodríguez, ; Scarcella et al, ; Zuur & Pierce, ), to groundwater and soil moisture dynamics (Kaplan & Muñoz‐Carpena, , ; Kaplan, Muñoz‐Carpena, & Ritter, ; Kovács, Márkus, & Gábor, ; Ritter & Muñoz‐Carpena, ), to large‐scale variation in rainfall and vegetation cover (Campo‐Bescós et al, ; Kuo, Chu, Pan, & Yu, ), and links between long‐term climate and tree growth (Linares & Camarero, ; Linares & Tíscar, ).…”

“…Initially developed for analysis of economic models (Engle & Watson, 1981;Geweke, 1977), DFA has more recently been applied to better understand a variety of physical and biological processes, from commercial fisheries production (Begoña Santos et al, 2012;Erzini et al, 2005;Pérez-Rodríguez, 2012;Scarcella et al, 2015;Zuur & Pierce, 2004), to groundwater and soil moisture dynamics (Kaplan & Muñoz-Carpena, 2011Kaplan, Muñoz-Carpena, & Ritter, 2010;Kovács, Márkus, & Gábor, 2004;Ritter & Muñoz-Carpena, 2006), to large-scale variation in rainfall and vegetation cover With DFA, temporal variation in a set of N observed time series is modelled as a linear combination of one to M common trends, zero to K explanatory variables, a constant intercept parameter, and noise (Zuur et al, 2003):…”

Unraveling the Hydrological Behavior of a Coastal Pond in Doñana National Park (Southwest Spain)

“…Mathematical methods applying time series analysis are increasingly being used to improve the understanding of surface water/groundwater interactions (Kaplan et al 2010;Aguilera et al 2013;Acworth et al 2015;Chiaudani et al 2017;Oh et al 2017;Haaf and Barthel 2018;Trásy et al 2018). Acworth et al (2015) successfully used Fourier analysis on daily and sub-daily scales to identify responses to evapotranspiration in hydraulic head fluctuations.…”

Combined Spatial and Temporal Effects of Environmental Controls on Long-Term Monthly NDVI in the Southern Africa Savanna

“…DFA was initially applied to economic time series [46][47][48][49] and was later extended to include explanatory variables in ecological systems. Because of its inherent advantages (efficiency, explanatory power, suitable for non-stationary data), it has since been applied successfully across a wide range of environmental systems, from the dynamics of squid populations [50] to variations in groundwater levels and quality [51][52][53][54], from soil moisture dynamics [55][56][57], to air quality [58] and, more recently, to link vegetation and climate dynamics [59][60][61].…”