Linking Flow Regime, Floodplain Lake Connectivity and Fish Catch in a Large River-Floodplain System, the Volga–Akhtuba Floodplain (Russian Federation)

Abstract

River-floodplain systems are amongst the most productive-but often severely impacted-aquatic systems worldwide. We explored the ecological response of fish to flow regime in a large riverfloodplain system by studying the relationships between (1) discharge and inundated floodplain area, with a focus on spatial and temporal patterns in floodplain lake connectivity, and (2) flood volume and fisheries catch. Our results demonstrate a non-linear relationship between discharge and floodplain inundation with considerable hysteresis due to differences in inundation and drainage rate. Inundation extent was mostly determined by flood volume, not peak discharge. We found that the more isolated lakes (that is, lakes with a shorter connection duration to the river) are located at higher local elevation and at larger hydrological distance from the main rivers: geographical distance to the river appears a poor predictor of lake isolation. Although year-to-year fish catches in the floodplain were significantly larger with larger flood volumes in the floodplain, they were not in the main river, suggesting that mechanisms that increase catch, such as increased floodplain access or increased somatic growth, are stimulated by flooding in the floodplain, but not in the river. Fish species that profit from flooding belong to different feeding guilds, suggesting that all trophic levels may benefit from flooding. We found indications that the ecological functioning of floodplains is not limited to its temporary availability as habitat. Refugia can be present within the floodplain itself, which should be considered in the management of large rivers and their floodplain.

Relative lake level fluctuations and their influence on productivity and resilience in tropical lakes and reservoirs

“…An understanding of these characteristics will help determine flow criteria for the maintenance of floodplain fish faunas and design appropriate flood curves that maximise benefits from the water available. Strong year-classes of fish tend to result from gradually increasing water levels combined with large flood volumes (Welcomme, 1975;Górski et al, 2011;van de Wolfshaar et al, 2011). Conversely, a constant high water level may decrease production because of the development of stagnant water conditions, with associated stratification and oxygen constraints (Junk et al, 1989;Karenge and Kolding, 1995).…”

The Numerical Simulation of Shallow Water: Estimation of the Roughness Coefficient on the Flood Stage

“…The existence of the Volga-Akhtuba Floodplain (VAF) is caused by the spring floods from May till June [1,2]. In this period, the amount of water coming from the Volga to the Akhtuba increases, and then the water flows into the network of channels and flooding low-lying areas of Floodplain [3].…”

Influence of hydrological connectivity on winter limnology in floodplain lakes of the Saskatchewan River Delta, Saskatchewan

“…As a result, many researchers have begun to use a combination of desktop and on-ground methods to accurately determine the connectivity of floodplain lakes (e.g., Wolfe et al 2007;van de Wolfshaar et al 2011;Ward et al 2013). Stable isotope composition of water samples from lakes within the SRD during the winter following a large summer flood event (2013)(2014) provided an effective validation of classifications based on remote sensing.…”

“…More conventional methods for determining connectivity of off-channel habitats include physical assessment of floodplain topography (Gibson et al 1996;Peters 2003) and monitoring of water balances (Mackay 1963;Marsh and Hey 1989), both heavily field-intensive methods. An alternative method, optical remote sensing, has proven successful in monitoring floodplain inundation in many tropical (Hess et al 2003;Ward et al 2013Ward et al , 2014 and temperate (Pavelsky and Smith 2009;van de Wolfshaar et al 2011;Long and Pavelsky 2013) systems. Optical remote sensing, however, can be limited by dense vegetation, smoke, and cloud cover, as they can obscure image clarity.…”