The intensity and frequency of droughts in Poyang Lake Basin have been increasing due to global warming. To properly manage water resources and mitigate drought disasters, it is important to understand the long-term characteristics of drought and its possible link with large-scale climate indices. Based on the monthly meteorological data of 41 meteorological stations in Poyang Lake Basin from 1958 to 2017, the spatiotemporal variations of drought were investigated using the standardized precipitation evapotranspiration index (SPEI). Ensemble empirical mode decomposition (EEMD) methods and the modified Mann–Kendall (MMK) trend test were used to explore the spatiotemporal characteristics and trends of drought. Furthermore, to reveal possible links between drought variations and large-scale climate indices in Poyang Lake Basin, the relationships between SPEI and large-scale climate indices, such as North Atlantic Oscillation (NAO), El Niño–Southern Oscillation (ENSO), Arctic Oscillation (AO), Indian Ocean Dipole (IOD) and Pacific Decadal Oscillation (PDO) were examined using cross-wavelet transform. The results showed that the SPEI in Poyang Lake Basin exhibited relatively stable quasi-periodic oscillation, with approximate quasi-3-year and quasi-6-year periods at the inter-annual scale and quasi-15-year and quasi-30-year periods at the inter-decadal scale from 1958 to 2017. Moreover, the Poyang Lake Basin experienced an insignificantly wetter trend as a whole at the annual and seasonal scales during the period of 1958–2017, except for spring, which had a drought trend. The special characteristics of the trend variations were markedly different in the basin. The areas in which drought was most likely to occur were mainly located in the Poyang Lake region, northwest and south of the basin, respectively. Furthermore, relationships between the drought and six climate indices showed that the drought exhibited a significant temporal correlation with five climate indices at restricted intervals, except for IOD. The dominant influences of the large-scale climate indices on the drought evolutions shifted in the Poyang Lake Basin during 1958–2017, from the NAO, Niño 3.4, and the Southern Oscillation Index (SOI) before the late 1960s and early 1970s, to the AO and PDO during the 1980s, then to the NAO, AO and SOI after the early 2000s. The NAO, AO and SOI exerted a significant influence on the drought events in the basin. The results of this study will benefit regional water resource management, agriculture production, and ecosystem protection in the Poyang Lake Basin.

To investigate the pollution characteristics of the surface sediments of the river–reservoir system in the Feiyun River basin, a sediment heavy metal survey was conducted for the first time in the Feiyun River basin. Surface sediments from 21 sampling sites in the Feiyun River basin were collected, and the concentrations and spatial distribution characteristics of 15 heavy metals (Cr, Ni, Cu, Zn, As, Cd, Pb, Mn, V, Co, Mo, Sb, W, Fe, and Se) were analyzed. Three heavy metal ecological risk assessment methods were used to evaluate the potential risks of heavy metals in sediments, and the sources of major heavy metals were traced by correlation analysis and principal component analysis. The results show that (1) the average concentration of heavy metals (As) (212.64 mg/kg) and (Sb) (4.89 mg/kg) in Feiyun River Basin is 33.3 and 6.89 times the background value of Zhejiang Province; the overall spatial distribution of heavy metals is: the mainstream of Feiyun River > Zhaoshandu Reservoir > Shanxi Reservoir, thereby, the pollution is relatively significant; (2) by processing the geo-accumulation index and enrichment index methods, As and Sb are classified as ‘severely polluted’, ‘moderately severely polluted’ and ‘severely polluted’, ‘very severe polluted’ respectively; (3) the potential ecological index evaluates the surface sediments in the Feiyun River Basin as a very high risk level, the main environmental risk factors are As, Sb, Cd and Mo; (4) the principal component analysis results show that the heavy metals in the sediments of the Feiyun River Basin may be mainly affected by human activities such as sewage from domestic and agricultural activities, mining and smelting, and the others are affected by natural factors.

Changes in the hydrological cycle and water resources are inevitable consequences of environmental change, and runoff is an important element of the hydrological cycle. Therefore, the assessment of runoff changes is crucial for water resources management and socio-economic development. As an inland river basin in the arid zone of northwest China, the Shiyang River Basin is very vulnerable to environmental changes. Consequently, this study evaluated the past runoff evolution of the Shiyang River basin using a variety of statistical tools. In addition, the improved Soil and Water Assessment Tool (SWAT) was used to predict runoff trends from 2019 to 2050 under potential future climate change and land use projection scenarios in the future for the Shiyang River Basin. In the inland river basins, water resources mainly come from headwaters of the rivers in the upper mountainous regions, where they are more sensitive. Therefore, this study not only examined the mainstream of the Shiyang River, but also the six tributaries in the upper stream. The results indicate that the mainstream of the Shiyang River Basin and its six upstream tributaries all showed declining trends from the 1950s to 2019, and most of the rivers will continue to insignificantly decrease until 2050. Furthermore, there are two main timescales for runoff in the past as well as future: one is around 40 years and another is 20–30 years. In the meantime, the Shiyang River and its tributaries have relatively consistent change characteristics. The results of this study will provide assistance to basin management agencies in developing more appropriate water resource management plans.

Global climate change and human activities have exacerbated droughts’ environmental and socioeconomic threats. However, there is still a lack of effective techniques to consider their combined impacts on drought identification. Therefore, a new copula-based multivariate standardized drought index (CMSDI) was proposed, which integrates precipitation data and terrestrial water storage anomaly (TWSA) data observed by Gravity Recovery and Climate Experiment (GRACE) satellites. The applicability of the CMSDI was assessed compared with the water storage deficits index (WSDI), the self-calibration Palmer drought severity index (sc-PDSI), the standardized precipitation evapotranspiration index (SPEI), and the standardized precipitation index (SPI) in the Yellow River Basin (YRB) and the Yangtze River Basin (YZRB) for 2002–2020. The assessments were conducted regarding both temporal evolution and spatial distribution. The results showed that the CMSDI was more synchronized with the WSDI and SPI than with the other two indices and presented different trends and correlations in the YRB and YZRB. The CMSDI outperformed the other drought indices due to the limitations of the sc-PDSI, SPEI, and SPI in detecting certain drought events, and the greater inaccuracy of the WSDI in identifying extreme droughts. Furthermore, the CMSDI revealed a clear upward trend in parts of the middle and lower YRB and a clear downward trend in the upper YZRB, emphasizing the need for more attention to droughts in the YRB. This study presents a new perspective on the integrated use of satellite and measured data in drought monitoring across different regions.

Accurate and punctual precipitation data are fundamental to understanding regional hydrology and are a critical reference point for regional flood control. The aims of this study are to evaluate the performance of three widely used precipitation datasets—CRU TS, ERA5, and NCEP—as potential alternatives for hydrological applications in the Bahr el Ghazal River Basin in South Sudan, Africa. This includes examining the spatial and temporal evolution of regional precipitation using relatively accurate precipitation datasets. The findings indicate that CRU TS is the best precipitation dataset in the Bahr el Ghazal Basin. The spatial and temporal distributions of precipitation from CRU TS reveal that precipitation in the Bahr el Ghazal Basin has a clear wet season, with June–August accounting for half of the annual precipitation and peaking in July and August. The long-term annual total precipitation exhibits a gradual increasing trend from the north to the south, with the southwestern part of the Basin having the largest percentage of wet season precipitation. Notably, the Bahr el Ghazal Basin witnessed a significant precipitation shift in 1967, followed by an increasing trend. Moreover, the spatial and temporal precipitation evolutions reveal an ongoing risk of flooding in the lower part of the Basin; therefore, increased engineering counter-measures might be needed for effective flood prevention.

Rainfall erosivity is a key factor to predict soil erosion rate in universal soil loss equation (USLE) and revised USLE (RUSLE). Understanding rainfall erosivity characteristics, especially its spatial distribution and temporal trends, is essential for soil erosion risk assessment and soil conservation planning. In this study, the spatial-temporal variation of rainfall erosivity in the Three Gorges Reservoir Area (TGRA) of China during 1960–2010, at annual and seasonal scales, was explored based on daily rainfall data from 40 stations (26 meteorological stations and 14 hydrologic stations). The Mann–Kendall test and Co–kriging interpolation method were applied to detect the temporal trends and spatial patterns. The results showed that TGRA’s annual rainfall erosivity increased from west, south, and east to the north-central, ranging from 3647.0 to 10884.8 MJ·mm·ha⁻¹·h⁻¹ with an average value of 6108.1 MJ·mm·ha⁻¹·h⁻¹. The spatial distribution of summer and autumn rainfall erosivity was similar to the pattern of annual rainfall erosivity. Summer is the most erosive season among four seasons, accounting for 53% of the total annual rainfall erosivity, and winter is the least erosive season. July is the most erosive month with an average of 1327.3 MJ·mm·ha⁻¹·h⁻¹, and January is the least erosive month. Mean rainfall erosivity was 5969.2 MJ·mm·ha⁻¹·h⁻¹ during 1960–2010, with the lowest value of 3361.0 MJ·mm·ha⁻¹·h⁻¹ in 1966 and highest value of 8896.0 MJ·mm·ha⁻¹·h⁻¹ in 1982. Mann–Kendall test showed that the annual rainfall erosivity did not change significantly across TGRA. Seasonal rainfall erosivity showed a significant decrease in autumn and insignificant decrease in summer and winter. Monthly rainfall erosivity in TGRA showed insignificant increases from Jun to Jul and then underwent decreases from Aug to Nov. and from Dec to Feb and it rose again in Feb reaching a 0.01 level significance. The daily rainfall data of supplemental stations is very useful to interpolate rainfall erosivity map, which could help to find the credible maximum and minimum value of TGRA. In total, the findings could provide useful information both for soil erosion prediction, land management practices, and sediment control project of TGRA.