1. Introduction
The notion of hydrographic connectivity emerged in the field of landscape ecology during the 1970s as a means of examining the correlation between landscape structure and biotic movements between patches, commonly referred to as landscape connectivity. In recent years, this concept has been increasingly adopted in hydrology and other domains [
1,
2,
3]. Hydrographic connectivity is defined as the capacity of hydrological cycle elements, such as material, energy, and biota, to migrate and transfer through water [
4]. Structural and functional connectivity are the two interrelated components that typify hydrographic connectivity. The former relates to the spatial continuity attributes of the landscape, which reflect static processes, while the latter pertains to the transfer and circulation of information, material, and energy that result from the interaction between the water body morphology and the connected body within the watershed, reflecting dynamic processes [
5,
6].
Currently, research on hydrographic connectivity mostly focuses on flood-prone areas, wetland plains, and small-scale watersheds such as lakes, deltas, and urban rivers, which has limited regional applicability. For instance, Cui et al. [
7] evaluated the hydrological connectivity of surface water in the Yellow River delta floodplain from 2006 to 2017 and found that the connectivity of the river system varied with the change in water body patches and spatial distribution; Dai et al. [
8] also investigated the hydrological connectivity of small-scale watersheds in the Yellow River delta using dye coverage, quantifying the interaction between hydrological connectivity and the soil–root complex; meanwhile, Lesschen et al. [
9] modeled hydrological and erosion processes in the Carcavo watershed in Spain using hydrological connectivity, exploring the influence of scale dependency on hydrological and erosion models; Liu et al. [
10] evaluated the hydrological connectivity of the Bositeng Lake from 1990 to 2019 using landscape connectivity methods and analyzed its response to human activities and climate change; Tan et al. [
11] developed a connectivity assessment tool, CAST 1.0, and assessed the response of hydrological connectivity to flow rate, water temperature, etc., using Poyang Lake as a case study; Tian et al. [
12] conducted a hydrological connectivity analysis and evaluation of the arid plains in the Dongliao River Basin using graph theory and binary water cycle theory. However, there is still a lack of literature on the response and changes of hydrographic connectivity in large-scale platforms and over long time spans concerning industrial and agricultural activities, and human social development. With the rapid development of society, the continuous changes in the environment and the crucial role of water environment in ecological systems and social development, it is urgent to evaluate hydrographic connectivity in large-scale watersheds and changing environments to enhance the understanding of watershed water environment protection and management.
In general, a digital elevation model (DEM) is utilized to determine the watershed scope for studying the spatio-temporal evolution of hydrographic connectivity [
13]. However, the traditional DEM-filling method may not be appropriate for studying hydrographic connectivity in flat downstream plain areas of rivers, such as the Henan section of the Yellow River Basin [
14,
15]. The conventional method for filling depressions in DEM typically relies on calculating the slope based on the height difference between the river and its banks, as well as the direction of the river flow. However, in flat riverine landscapes, where the riverbed is at or above the level of the riverbanks, traditional DEM-based methods are unsuitable for water feature extraction [
7,
16]. Therefore, an alternative approach known as the water index method has been introduced. These methods utilize the differences in reflectance of specific bands in remote sensing images to calculate and filter out water bodies, without relying on slope information [
17]. McFeeters [
18] originally derived the normalized difference water index (NDWI) in 1996 from the normalized difference vegetation index (NDVI) as a method for processing specific bands to highlight water information and suppress vegetation information. After several years of development, various improvements have been made, such as the modified normalized difference water index (MNDWI) and the new water index (NWI), providing multiple avenues for water extraction [
19].
The Yellow River Basin, specifically in Henan province, plays a crucial role in providing ecological services to the local community. However, its unique hydrological conditions, characterized by high riverbed elevation above the ground level, have limited research and development on the local hydrographic connectivity. In the last two decades, due to the rapid urbanization and population growth in the basin area with hundreds of millions of people living in a city cluster, the utilization and preservation of water resources have become increasingly pressing issues owing to the region’s hydrological scarcity. To better elucidate the impact of urbanization and modern water resource utilization changes on the native hydrological network in the studied urban area, we employed supervised classification and the MNDWI method to enhance the accuracy of water body extraction from remote sensing imagery in the plain river and lake network region. Additionally, we employed a Set Pair Analysis method to construct an evaluation framework for assessing the hydrographic connectivity in the study area. Following an examination of the interrelationships between changes in hydrographic connectivity and human activities during the period from 2000 to 2020, this research aims to provide theoretical support and reference points for sustainable water resource allocation in the Yellow River Basin and other basins, facilitating the optimized utilization of resources.
4. Conclusions
Between 2000 and 2020, the land use composition in the Henan section of the Yellow River Basin underwent significant changes, driven primarily by urbanization and socio-economic development. Agricultural land emerged as the dominant land-use type during this period, while rivers and reservoirs were the least utilized. Notably, the expansion of built-up areas experienced substantial growth, altering the landscape of the study area.
During this timeframe, the implementation of various hydraulic engineering projects and water resource protection policies contributed to the improved structural form and enhanced integrity of the water system in the study area. However, the construction of diversion canals and the sediment accumulation in the mainstream reservoirs resulted in a reticular water system with reduced sediment transport capacity, limited material exchange between nodes, and decreased overall connectivity. It was only after 2010, with the introduction of reservoir regulation and ecological environment protection measures, that the connectivity of the water system stabilized.
The functional connectivity of the water system in the study area exhibited a gradual and positive trend, reaching a satisfactory level overall. In the early 21st century, the natural function was rated as medium but steadily improved to a good level while remaining stable. On the other hand, the social function initially faced challenges such as interrupted flow and high sediment content, leading to a poor rating. However, as the structural connectivity of the water system increased, the social function also improved and eventually achieved a good rating. The integrated functional connectivity of the water system in the study area, primarily influenced by the social function, progressively improved over the 20-year period, culminating in an optimal rating in 2020. This signifies that the functional connectivity of the water system in the study area has consistently advanced over the past two decades, reaching its peak in 2020.
Within the study area, the agricultural sector emerged as the largest water consumer, with domestic water usage experiencing the most significant growth. Although per capita water consumption decreased due to population expansion, it still exceeded the national average. Despite the pressures exerted on the water system by urban expansion and population growth, recent changes in the water usage structure have had a positive impact on the functional connectivity of the water system in the study area.
It is suggested that future work can be carried out in the following aspects: First, by improving the accuracy of front-end data sources. The spatial and temporal limitations of remote sensing interpretation of water body and water system data have restricted the overall accuracy of the framework. Second, from the perspective of integrating management and theory, by combining relevant evaluation and optimization frameworks with big data platforms to construct a watershed management intelligent platform, which can provide real-time data prediction for comprehensive decision-making by management departments.