Determining the dynamics of surface hydrological connectivity in a landscape of multiple lakes with different sizes and depths is challenging. This is especially the case for ungagged, large areas of multi-lake systems. Integrated use of remote sensing and geostatistical analysis can be a useful approach for developing metrics that can be used to identify the hydrological connectivity and their changes. In this study, we conducted a geostatistical analysis of 18 wet and dry binary state rasters derived from Landsat images over a large ungauged multi-lake system, the Momoge National Nature Reserve in Northeast China. Our goal was to investigate applicability and dynamics of three surface hydrological connectivity metrics, namely, geostatistical connectivity function (GCF), maximum distance of connection (MDC), and surface water extent (SWE) of the top 10 largest connectomes (i.e., seasonally connected water bodies). We found that, during a dry year, the reduction rate of the GCF curve was slower along the west–east (W–E) direction than along the north–south (N–S) direction, which was contrary to the patterns exhibited in a normal or wet year. The minimum values of the MDC in W–E and N–S directions in the dry year were 22.4 km and 6.3 km, respectively, while the maximum values of the MDC along the above two directions in the wet year were 50.7 km and 65.1 km, respectively. The components and spatial distribution of the top 10 largest connectomes changed dramatically in different months of each hydrological year, resulting in a huge change in the monthly SWE of the top 10 largest connectomes. Overall, this study validated the usefulness of combining remote sensing image analysis with geostatistical methods to quantify the surface hydrological connectivity from different perspectives in an ungauged area. The approach may be applicable to studies in other geographical regions, to guide water resources and wetland management practices.
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