2019
DOI: 10.1029/2018wr023897
|View full text |Cite
|
Sign up to set email alerts
|

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… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

1
32
0

Year Published

2019
2019
2021
2021

Publication Types

Select...
5
2
1

Relationship

5
3

Authors

Journals

citations
Cited by 36 publications
(33 citation statements)
references
References 57 publications
1
32
0
Order By: Relevance
“…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.…”
Section: Water Circulation Pattern: Seasonal and Spatial Distributionsupporting
confidence: 87%
“…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.…”
Section: Water Circulation Pattern: Seasonal and Spatial Distributionsupporting
confidence: 87%
“…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).…”
Section: Process-based Hydrologic Modelmentioning
confidence: 99%
“…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.…”
Section: Introductionmentioning
confidence: 99%
“…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).…”
Section: Introductionmentioning
confidence: 99%