Droughts can severely reduce the productivity of agricultural lands and forests. The United States Department of Agriculture (USDA) Southeast Regional Climate Hub (SERCH) has launched the Lately Identified Geospecific Heightened Threat System (LIGHTS) to inform its users of potential water deficiency threats. The system identifies droughts and other climate anomalies such as extreme precipitation and heat stress. However, the LIGHTS model lacks input from soil moisture observations. This research aims to develop a simple and easy-to-interpret soil moisture and drought warning index-standardized soil moisture index (SSI)-by fusing the space-borne Soil Moisture Active Passive (SMAP) soil moisture data with the North American Land Data Assimilation System (NLDAS) Noah land surface model (LSM) output. Ground truth soil moisture data from the Soil Climate Analysis Network (SCAN) were collected for validation. As a result, the accuracy of using SMAP to monitor soil moisture content generally displayed a good statistical correlation with the SCAN data. The validation through the Palmer drought severity index (PDSI) and normalized difference water index (NDWI) suggested that SSI was effective and sensitive for short-term drought monitoring across large areas.

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The famous 'Hu Line', proposed by Hu Huanyong in 1935, divided China into two regions (southeast and northwest) of comparable area size but drastically different in population. However, the classic Hu Line was derived manually in absence of reliable census data and computational technologies of modern days. It has been subject to criticism of lack of scientific rigor and accuracy. This research uses a GIS-automated regionalization method, termed REDCAP (Regionalization with Dynamically Constrained Agglomerative Clustering and Partitioning), to reconstruct the demarcation line based on the 2010 county-level census data in China. The results show that the logarithmic transformation of population density is a better measure of attributive homogeneity in derived regions than density itself, and produces two regions of nearly identical area size and greater contrast in population. Specifically, the revised Hu Line by Hu Huanyong in 1990 had the southeast region with 94.4% of total population and 42.9% of total land, and our delineation line yields a southeast region with 97.4% population and 50.8% land. Therefore, the population density ratio of the two regions is 27.1 by our line, much higher than the ratio of 22.4 by the Hu Line, and thus outperforms the Hu Line in deriving regions of maximum density contrast with comparable area size. Furthermore, more regions are delineated to further advance our understanding of population distribution disparity in China.

The stunning disparity in population density between the southeast and northwest in China is highlighted by the “Hu Line,” a famous population demarcation line proposed by Huanyong Hu in 1935. This research seeks to uncover the underlying physical environment factors that shape such a contrast. Specifically, we propose a habitation environment suitability index (HESI) model to integrate topographic factors, climatic suitability, and hydrological condition into one comprehensive index, and then use a GIS‐automated regionalization method termed REDCAP (Regionalization with Dynamically Constrained Agglomerative Clustering and Partitioning) to derive two demarcation lines based on the HESI and population density values, respectively. The two lines that divide China into two regions are largely consistent with each other. The result indicates that the population distribution disparity between the southeast and northwest is largely attributable to the difference in physical environments, and the barrier defined by the Hu Line is here to stay. In addition, the research also explores the (in)consistency between population density and HESI distribution patterns in various regions.