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Open AccessArticle

Integrating the JRC Monthly Water History Dataset and Geostatistical Analysis Approach to Quantify Surface Hydrological Connectivity Dynamics in an Ungauged Multi-Lake System

1
School of Science, Anhui Agricultural University, 130 Changjiangxilu, Hefei 230036, China
2
School of Geographic Sciences/Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution, Xinyang Normal University, 237 Nanhu Road, Xinyang 464000, China
3
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
4
School of Renewable Natural Resources, Louisiana State University Agricultural Center, 227 Highland Road, Baton Rouge, LA 70803, USA
5
Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, China
*
Author to whom correspondence should be addressed.
Academic Editor: Leonardo V. Noto
Water 2021, 13(4), 497; https://doi.org/10.3390/w13040497
Received: 5 February 2021 / Revised: 10 February 2021 / Accepted: 12 February 2021 / Published: 14 February 2021
(This article belongs to the Section Hydrology and Hydrogeology)
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. View Full-Text
Keywords: hydrological connectivity; geostatistical analysis; remote sensing; Landsat; lake; wetland; Momoge National Nature Reserve hydrological connectivity; geostatistical analysis; remote sensing; Landsat; lake; wetland; Momoge National Nature Reserve
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MDPI and ACS Style

Wu, L.; Chen, Y.; Zhang, G.; Xu, Y.J.; Tan, Z. Integrating the JRC Monthly Water History Dataset and Geostatistical Analysis Approach to Quantify Surface Hydrological Connectivity Dynamics in an Ungauged Multi-Lake System. Water 2021, 13, 497. https://doi.org/10.3390/w13040497

AMA Style

Wu L, Chen Y, Zhang G, Xu YJ, Tan Z. Integrating the JRC Monthly Water History Dataset and Geostatistical Analysis Approach to Quantify Surface Hydrological Connectivity Dynamics in an Ungauged Multi-Lake System. Water. 2021; 13(4):497. https://doi.org/10.3390/w13040497

Chicago/Turabian Style

Wu, Lili; Chen, Yueqing; Zhang, Guangxin; Xu, Y. J.; Tan, Zhiqiang. 2021. "Integrating the JRC Monthly Water History Dataset and Geostatistical Analysis Approach to Quantify Surface Hydrological Connectivity Dynamics in an Ungauged Multi-Lake System" Water 13, no. 4: 497. https://doi.org/10.3390/w13040497

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