Understanding the spatiotemporal characteristics of regional drought is of great significance in decision-making processes such as water resources and agricultural systems management. The North China Plain is an important grain production base in China and the most drought-prone region in the country. In this study, the monthly standardized precipitation evapotranspiration index (SPEI) was used to monitor the spatiotemporal variation of agricultural drought in the North China Plain from 1960 to 2017. Seven spatial patterns of drought variability were identified in the North China Plain, such as Huang-Huai Plain, Lower Yangtze River Plain, Haihe Plain, Shandong Hills, Qinling Mountains Margin area, Huangshan Mountain surroundings, and Yanshan Mountain margin area. The spatial models showed different trends in different time stages, indicating that the drought conditions in the North China Plain were complex and changeable in the past 58 years. As an important agricultural area, the North China Plain needs more attention since this region shows a remarkable trend of drought and, as such, will definitely increase the water demand for agricultural irrigation. The strong correlation between these spatial distribution patterns indicates that the climate and weather conditions leading to drought are consistent and that drought conditions are independent for regions that are not correlated. If this trend continues, the characteristics of drought variability in the North China Plain will become more complex, and a more detailed water management strategy will be needed to address the effects of drought on agro-ecosystems. Recognizing the drought variability in the North China Plain can provide a basis for agricultural disaster reduction planning and water resources allocation.

Regional climate variability assessment is of great significance in decision-making such as agriculture and water resources system management. The identification of sub-regions with similar drought variability can provide a basis for agricultural disaster reduction planning and water resource distribution. In this research, a modified daily Standardized Precipitation Evapotranspiration Index (SPEI) was used to monitor the spatial and temporal variation characteristics of agricultural drought in the North China Plain from 1960 to 2017, which was studied by using the rotated empirical orthogonal functions (REOF). Through the seasonal REOF process, 7–9 seasonal drought sub-regions are confirmed by applying time series and the correlation relationship of SPEI original data. The strong correlation of these sub-regions indicates that the climate and weather conditions causing the drought are consistent and the drought conditions are independent for the regions that show no correlation. In general, the results of the seasonal trend analysis show that there has been no significant trend value in most areas since 1960. However, it is worth noting that some regions have the positive and negative temporal trends in different seasons. These results illustrate the importance of seasonal analysis, particularly for agro-ecosystems that depend on timely rainfall during different growing seasons. If this trend continues, seasonal drought will become more complex, then a more elaborate water management strategy will be needed to reduce its impact.

The Bailong River Basin is one of the most developed regions for debris flow disasters worldwide, often causing severe secondary disasters by blocking rivers. Therefore, the early identification of potential debris flow disasters that may block the river in this region is of great significance for disaster risk prevention and reduction. However, it is quite challenging to identify potential debris flow disasters that may block rivers at a regional scale, as conducting numerical simulations for each debris flow catchment would require significant time and financial resources. The purpose of this article is to use public resource data and machine learning methods to establish a relationship model between debris flow-induced river blockage and key influencing factors, thereby economically predicting potential areas at risk for debris flow-induced river blockage disasters. Based on the field investigation, data collection, and remote sensing interpretation, this study selected 12 parameters, including the basin area, basin height difference, relief ratio, circularity ratio, landslide density, fault density, lithology index, annual average frequency of daily rainfall exceeding 40 mm, river width, river discharge, river gradient, and confluence angle, as critical factors to determine whether debris flows will cause river blockages. A relationship model between debris flow-induced river blockage and influencing factors was constructed based on machine learning algorithms. Several machine learning algorithms were compared, and the XGB model performed the best, with a prediction accuracy of 0.881 and an area under the ROC curve of 0.926. This study found that the river width is the determining factor for debris flow blocking rivers, followed by the annual average frequency of daily rainfall exceeding 40 mm, basin height difference, circularity ratio, basin area, and river discharge. The early identification method proposed in this study for river blockage disasters caused by debris flows can provide a reference for the quantitative assessment and pre-disaster prevention of debris flow-induced river blockage chain risks in similar high-mountain gorge areas.

Based on the origin data of Landsat TM images in 1980, 1995, 2000, 2006, 2012, this paper recurred to geoscience information spectrogram theory and utlized RS / GIS technologies, land use dynamic degree , land use transition matrixes and CA-Markov model to analyze the land use pattern change in Shiyang River Basin in the past 33 years, which was for the sake of discussing the spatial-temporal evolution rules and driving mechanism of land use in Shiyang River Basin. The results showed that: (1) There were apparent changes in the land use of Shiyang River Basin form 1980 to 2013. (2) in the past 33 years, the dynamic degrees of forest land, urban and rural resident construction land had relative large increase degree on the whole. (3) The main transition direction of land use types were arable land to grass land, grass land to unused land and unused land to arable land and forest land. (4) The CA-Markov model forecasting results indicated that there was no obvious change in land use construction in 2020. (5) The change of land use/cover brought desertification, mountainous runoff variation, the loss of water environment, vegetation coverage, water and soil, and a series of ecological environment problems.

In the context of the rural revitalization strategy, an accurate grasp of the spatial differentiation characteristics and influencing factors of rural settlements in underdeveloped arid inland river basins is urgently required. Taking the Shiyang River Basin as an example, rural settlements from 2000 to 2019 were examined via visual interpretation using satellite remote sensing data and official statistical data. Following the logic of “state characteristics—evolutionary patterns—influence factors—layout optimization”, the average nearest index, the gravity-center migration model, spatial statistical analysis, and other methods were used in combination with GIS. The spatiotemporal pattern evolution characteristics of rural settlements in the past 20 years were analyzed. The results revealed the following: (1) The distribution pattern of rural settlements in the study area is sheet-like and strip-shaped. The projects in the southeast are mostly distributed in a patchy pattern with high density, while the characteristics in the west and north are exactly opposite. The objects in the south are distributed in an alluvial area of rivers, while settlements in the north are located in an oasis area. (2) From 2000 to 2019, the scale of rural settlements in the Shiyang River Basin, where there is a simultaneous occurrence of newly-built and disappearing phenomena, shows an expansion trend that first increased quickly and then slowed down. Spatially, rural settlements in the basin show a clustering trend toward the southwest. (3) The distribution characteristics of rural settlements are close to water and roads, and they are greatly influenced by urban–rural integration and ecological migration. The results will provide a scientific basis for accelerating the modernization of rural areas and the construction of new rural areas according to local conditions.