Seasonal and Interannual Patterns and Controls of Hydrological Fluxes in an Amazon Floodplain Lake With a Surface‐Subsurface Process Model

Abstract

Floodplain lakes represent important aquatic ecosystems, and field‐based estimates of their water budgets are difficult to obtain, especially over multiple years. We examine the hydrological fluxes for an Amazon floodplain lake connected to the Solimões River using a process‐based hydrologic model. Water exchanges between the river and lake agree well with field estimates, including the timing of different hydrological phases. However, beyond available field data, modeling results show that the seven simulated years all differed from each other. These interannual differences were caused by the interplay between phases when water levels were rising with river‐water flowing into the lake (RWRI), versus rising with lake‐water flowing out to the river (RWLO). This exchange determines the river‐water content in the lake (CL). Maximum CL occurred before river levels peaked because local catchment contributions can be sufficient to push lake‐water out to the river, even as river levels rise. Numerical experiments show that the seasonal distribution of local rainfall, local catchment size, and interannual variability in both climate and river stage can contribute to differing dynamics of CL in a floodplain lake. Their impacts vary among phases: river‐rise dominates the RWRI, whereas local hydrological processes dominate the RWLO and receding‐water phases. Intermediate‐to‐long‐term rainfall accumulation controls CL during the RWLO phase, whereas annual precipitation accumulation is important for CL during low water. Our model generalizes beyond limited available field studies and offers potential to better understand floodplain lakes in other areas and how regional versus local changes in climate may affect their hydrological dynamics.

Flooding Dynamics Within an Amazonian Floodplain: Water Circulation Patterns and Inundation Duration

“…Our findings support Alsdorf et al (2007) statement upon the difficulty to retrieve water circulation from FP topography only. They also confirm those obtained by Ji et al, (2019) on another Amazonian FP that highlighted a strong interplay between local rainfall/runoff and river rising/receding rate.…”

Revealing Causal Controls of Storage-Streamflow Relationships With a Data-Centric Bayesian Framework Combining Machine Learning and Process-Based Modeling

“…To be able to conduct causal experiments, we employed the Process-based Adaptive Watershed Simulator coupled with the Community Land Model (PAWS+CLM) (Shen and Phanikumar, 2010;Shen et al, 2013Shen et al, , 2014Shen et al, , 2016Ji et al, 2015Ji et al, , 2019Niu et al, 2017;Ji and Shen, 2018;Fang et al, 2019). First introduced in Shen and Phanikumar (2010), the PAWS model was coupled to the Community Land Model (CLM) (Collins et al, 2006;Dickinson et al, 2006;Oleson et al, 2010;Lawrence et al, 2011) which describes the land surface and vegetation dynamics (Shen et al, 2013).…”

“…It is easy to see that paradigms like model calibration (Vrugt et al, 2003) or Monte Carlo Markov Chain (Vrugt et al, 2009) fit into this framework. Moreover, numerical experiments where the modelers perturb model physics on an ad-hoc basis (e.g., Maxwell and Condon, 2016;Shen et al, 2016;Ji et al, 2019) could also be placed in this framework. Potential issues with this framework are that it can be both subjective and inefficient, as many competing hypotheses remain un-tested.…”

Limnological perspectives on conservation of floodplain lakes in the Amazon basin

“…Inflows of nutrient‐rich water from the Amazon River, other tributaries and local catchments help sustain lake productivity (Melack & Forsberg, 2001). Complex flow patterns (Alsdorf, Bates, Melack, Wilson, & Dunne, 2007) and differences in the sources of water (Bonnet et al, 2017; Ji et al, 2019; Lesack & Melack, 1995; Rudorff, Melack, & Bates, 2014) account, in part, for variations in the levels of nutrients and productivity both within and among lakes (Forsberg, Devol, Richey, Martinelli, & Santos, 1988; Forsberg, Melack, et al, 2017). Log–log relationships between mean annual chlorophyll and total phosphorus (TP) and total nitrogen (TN; linear regressions, r 2 = 0.85 and 0.88, respectively), indicate that both of these nutrients can limit phytoplankton biomass in these lakes (Trevisan & Forsberg, 2007).…”