Assessing the impact of climate change on floodplain productivity poses unique challenges for hydrodynamic models. For example, the dynamics of floodplain fisheries are governed both by inundation dynamics across thousands of km2, and water storage timing within small depressions (which serve as fish habitat) connected to the river network by meter-scale manmade canals, controlled by flow across fishing weirs. Here, we propose to represent these features as a system of effective, interconnected sub-grid elements within a coarse-scale model. We test this strategy over the Logone floodplain in Cameroon, and its floodplain fishery. We first validate this strategy for a local study area (30 km2); we find that hydraulic models at resolutions from 30 m to 500 m are able to reproduce hydraulic dynamics as documented by in situ water level observations. When applied to the entire floodplain (16,000 km2), we find that the proposed modeling strategy allows accurate prediction of observed pattern of recession in the depressions. Artificially removing floodplain canals in the model causes residence time of water in depressions to be overpredicted by approximately 30 days. This study supports the strategy of modeling fine-scale interconnected features as a system of sub-grid elements in a coarse resolution model for applications such as assessing the sensitivity of floodplain fisheries to future climate change.
|Journal||Journal of Hydrology|
|Publication status||Published - 2020 Sept|
Bibliographical noteFunding Information:
This study was funded by the National Science Foundation grant, Dynamics of Coupled Natural and Human Systems (CNH) Program: Exploring social, ecological and hydrological regime shifts in the Logone Floodplain, Cameroon, Mark Moritz (PI), Michael Durand, Ian Hamilton, Bryan Mark, Ningchuan Xiao (BCS‐1211986). We are grateful to Sara Vassolo for discharge data, and the CARPA field team for field data collection.
© 2020 Elsevier B.V.
All Science Journal Classification (ASJC) codes
- Water Science and Technology