资源环境科技发展态势分析平台

Wetlands are an important habitat for many species but over the past few decades ecosystem biodiversity and function have been threatened. Due to their shallow and fluctuating water levels, wetlands are particularly vulnerable to climate variability. This is especially a risk for ephemeral and intermittent wetlands with limited hydrologic connections to deep aquifers, designated herein as Climate-induced Intermittent Wetlands (CiIWs). However, the response of CiIW systems to long-term climate variability has received limited research attention, partly because continuous ground surface monitoring data is rarely available over inter-decadal periods. An alternative to ground surface data is the use of satellite imagery to estimate the temporal water extent variability.

An integrated remote sensing and modeling approach is presented here to provide a novel method for investigating historical water storage variations in a CiIW system. The new method estimates water levels in a shallow wetland using Landsat data and was successfully validated against field water level data. The new method performed better than five existing algorithms. A water balance model was calibrated using the combined remotely sensed and local field data to derive daily water level time series since 1900. The validated water balance model results indicated that most of the water level fluctuations in the intermittent wetland can be explained by climatic drivers and subsurface flow interactions. Overall, this study demonstrates the importance of an integrated remote sensing and water balance modeling approach for hydroclimatic analysis of intermittent wetlands.

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