Drought is one of the most frequent and most widespread natural disasters worldwide, significantly impacting agricultural production and the ecological environment. An investigation of long-term drought changes and its influencing factors provides not only an understanding of historical droughts but also a scientific basis for the protection of future water resources. This study investigated the temporal characteristics of drought in a study site located in the center of Southwest China (SWC) over a 700-year period (AD 1300–2005) using the Palmer Drought Severity Index (PDSI). The linkage between drought and its influencing factors is discussed. An algorithm based on the random forest (RF) method was proposed to analyze the dynamic influence of the factors on drought. We also examined the linkages between the demise of two dynasties and historical drought events. The results showed that the study site was a drought-prone area in the study period and experienced a non-significant drying trend in all centuries, except for the 17th century; a total of 232 droughts were detected in the study site from AD 1300–2005. The wavelet spectrum of the PDSI series showed the existence of 4-, 8-, 16-, 32-, and 128-year-periods. A strong correlation existed between the sunspot numbers and the PDSI. The correlation of the period between the PDSI and El Niño-Southern Oscillation (ENSO) series in the same frequency domain was weak, while the ENSO exhibited a strong interaction with the PDSI in some time periods. The Pacific Decadal Oscillation (PDO) and PDSI had no resonance period in the low-frequency region, but there was a period of 80–130 years in the high-frequency region. The relative rates of influence of the ENSO, sunspot numbers, and PDO during AD 1700–1996 were 38.40%, 31.81%, and 29.8%, respectively. However, the mechanism of the interaction between droughts and the influential factors is complex, and the dominant factor changed over time. The analysis of long-term drought changes based on the PDSI series may provide clues to understand the development of historical events.

Water quality evaluation is an essential measure to analyze water quality. However, excessive randomness and fuzziness affect the process of evaluation, thus reducing the accuracy of evaluation. Therefore, this study proposed a cloud model for evaluating the water quality to alleviate this problem. Analytic hierarchy process and entropy theory were used to calculate the subjective weight and objective weight, respectively, and then they were coupled as a combination weight (CW) via game theory. The proposed game theory-based cloud model (GCM) was then applied to the Qixinggang section of the Beijiang River. The results show that the CW ranks fecal coliform as the most important factor, followed by total nitrogen and total phosphorus, while biochemical oxygen demand and fluoride were considered least important. There were 19 months (31.67%) at grade I, 39 months (65.00%) at grade II, and one month at grade IV and grade V during 2010–2014. A total of 52 months (86.6%) of GCM were identical to the comprehensive evaluation result (CER). The obtained water quality grades of GCM are close to the grades of the analytic hierarchy process weight (AHPW) due to the weight coefficient of AHPW set to 0.7487. Generally, one or two grade gaps exist among the results of the three groups of weights, suggesting that the index weight is not particularly sensitive to the cloud model. The evaluated accuracy of water quality can be improved by modifying the quantitative boundaries. This study could provide a reference for water quality evaluation, prevention, and improvement of water quality assessment and other applications.

Drought imposes serious challenges to ecosystems and societies and has plagued mankind throughout the ages. To understand the long-term trend of drought in China, a series of annual self-calibrating Palmer drought severity indexes (scPDSI), which is a semi-physical drought index based on the land surface water balance, were reconstructed during AD 56~2000. Multi-proxy records of tree-ring width and stalagmite oxygen isotope δ¹⁸O were used for this reconstruction, along with random forest regression. The spatiotemporal characteristics of the reconstruction results were analyzed, and comparisons were made with previous studies. Results showed that (1) China witnessed a drought-based state during the past 2000 years (mean value of scPDSI was −0.3151), with an average annual drought area of 85,000 km²; 4 wetting periods, i.e., the Han Dynasty (AD 56~220), the Tang Dynasty (AD 618~907), the Ming Dynasty (AD 1368~1644), and the Qing Dynasty (AD 1644~1912); and 2 drying periods, i.e., the Era of Disunity (AD 221~580) and the Song Dynasty (AD 960~1279). (2) Three different alternating fluctuation dry-wet modes (i.e., interannual, multidecadal, and centennial scales) in China were all significantly (p-value < 0.001) correlated with the amplitude and frequency of temperature in the Northern Hemisphere. (3) According to the spatial models disassembled from the rotated empirical orthogonal function, China was divided into nine dry-wet regions: northwestern China, Xinjiang, southwestern China, southeastern China, the Loess plateau, central China, southwestern Tibet, eastern China, and northeastern China. (4) The random forest (RF) was found to be accurate and stable for the reconstruction of drought variability in China compared with linear regression.