Under climate change, the frequency of drought-flood abrupt alternation (DFAA) events is increasing in Southeast China. However, there is limited research on the evolution characteristics of DFAA in this region. This study evaluated the effectiveness of the drought and flood indexes including SPI (Standardized Precipitation Index), SPEI (Standardized Precipitation Evapotranspiration Index), and SWAP (Standardized Weighted Average Precipitation Index) in identifying DFAA events under varying days of antecedent precipitation. Additionally, the evolution characteristics of DFAA events in Fujian Province from 1961 to 2021 were explored. The results indicate that (1) SPI-12d had the advantages of high effectiveness, optimal generalization accuracy, and strong generalization ability of identification results, and it can be used as the optimal identification index of DFAA events in Southeast China. (2) There was an overall increase in DFAA events at a rate of 1.8 events/10a. The frequency of DFAA events showed a gradual increase from the northwest to the southeast. (3) DTF events were characterized by moderate drought to flood, particularly in February, July, and August, while FTD events were characterized by light/moderate flood to drought, with more events occurring from June to October. (4) DTF event intensity increased in the northern and western regions from 1961 to 2021. For FTD events, the intensity notably increased in the western region from 1961 to 2001, while a significant increase occurred in all regions except the central region from 2001 to 2021. These findings emphasize the need for precautionary measures to address the increasing frequency and severity of DFAA events in Southeast China.

Reservoirs are an important nitrogen sink as a result of their retention effect, but their retention performance may vary with hydrologic conditions with time-varying characteristics, which also change them from being a sink to source over time. This study uses a coupled modelling system (Soil and Water Assessment Tool (SWAT) and a two-dimensional hydrodynamic and water quality model (CE-QUAL-W2) to analyze the nitrogen retention effect and influential factors at annual, monthly, and daily scales in Shanmei Reservoir in southeast China. The results showed that there was a positive retention effect of total nitrogen (TN), nitrate-nitrogen (NO₃-N) and ammonia nitrogen (NH₄-N) in most years, with average retention rates up to 12.7%, 7.83% and 26.17%, respectively. The reservoir serves mainly as a nitrogen sink at an annual scale. The monthly retention performances of TN and NO₃-N were observed during the wet season (April–October) with higher water temperature and lower velocity, while a release effect occurred during the dry season (November–March). For NH₄-N, which is prone to nitrification, the retention effect lasted longer, from May to December. The daily nitrogen retention process changed more dramatically, with the retention rate varying from −292.49 to 58.17%. During the period of dispatch, the regulated discharge was the primary factor of daily retention performance, while the hydraulic residence time, velocity and water level were all significantly correlated with nitrogen retention during the period without dispatch.

Evapotranspiration is one of the critical processes in the terrestrial hydrological cycle, and the assessment of evapotranspiration is essential for understanding the regional hydrological cycle. In this study, the Minjiang River Basin, a typical watershed in the humid subtropical climate zone, is selected as the study region. The Penman-Monteith equation and the dual crop coefficient method are used to calculate the actual evapotranspiration (ET_a) at seven meteorological stations within the study basin. Meanwhile, the applicability of the Global Land Data Assimilation System-Noah (GLDAS-Noah) ET_a data in the Minjiang River Basin is evaluated based on stations P-M equation results, then to analyze the changes of the ET_a in the Minjiang River Basin from 2000 to 2019. The results show that the GLDAS-Noah ET_a data are well applicable in the Minjiang River Basin (R² > 0.9 and NSE > 0.8). The ET_a in the basin shows an increasing trend since 2000, and the increasing rate is 3.60 mm·yr⁻¹ (p < 0.01). The seasonal variation results show that ET_a tends to increase in winter and spring, with increasing rates of 1.10 mm·yr⁻¹ (p < 0.01) and 2.60 mm·yr⁻¹ (p < 0.01), respectively, while the ET_a did not change significantly in summer and autumn. Annual air temperature has the largest effect on annual ET_a (59.6%), followed by precipitation at 33.9%. ET_a increased in spring was mainly influenced by increasing temperatures (89.4%) in the Minjiang River Basin from 2000 to 2019. The research results are of great benefit to further improve the understanding of ET_a variations in the basin under global warming.

Water scarcity has become a global severe challenge over the past few decades. Quantifying the impact of climate variability and land use on water resource availability is crucial for integrated water resource management. Many studies have focused on blue water but ignored green water which is important in the terrestrial ecosystem, especially on different temporal scales. In this study, we selected the Shanmei Reservoir, the most import drinking water resource for a rapidly development city of Southeast China, as a case for analysis of these impacts for the entire basin. We adopted the Soil and Water Assessment Tool (SWAT) to investigate the spatial and temporal distributions of blue water (BW), green water flow (GWF) and green water storage (GWS) in the Shanmei Reservoir Basin (SRB). The results of the blue and green water components (BW and GW) revealed that SRB is dominated by BW, accounting for 52.6% of the total water resources, while GW accounted for 47.4%. There was an insignificant upward trend of BW and a significant upward trend of GWF, with a tendency rate of 1.125 mm a⁻¹. Precipitation was the key factor affecting BW on annual and monthly scales. The GWF was more sensitive to temperature at both the annual and monthly scales. The GWS was significantly correlated with precipitation at the monthly scale, while insignificant correlation occurred at the annual scale. The spatial distribution of BW was largely dominated by precipitation, and land-use types led to the differentiation of GW. It indicates that the BW of paddy fields is greater than that of forests, while the GWS of forests is greater than that of orchards and rainfed croplands.

A reasonable rain gauge network can provide valid precipitation information that reflects the spatial and temporal fluctuation characteristics for a given basin. Thus, it is indispensable for designing an optimal network with a minimal number of rain gauges (NRGs) in an optimal location as a means of providing reliable rainfall records, both in terms of the areal average rainfall and the spatiotemporal variability. This study presents a methodological framework that couples the ordinary kriging (OK) method and spatial correlation approach (SCA) to optimize current rain gauge networks, which involves the deletion of redundant gauges and the addition of new rain gauges in the ‘blank’ monitoring area of a basin. This framework was applied to a network of 38 rain gauges in the Jinjiang Basin in southeast China. The results indicated that: (1) the number of rain gauges was reduced from 38 to 11 by using the OK method to determine the redundant rain gauges, which were removed to obtain the ‘base’ rain gauge network. The base rain gauges were mainly distributed in the midstream of this basin. (2) The SCA and OK were employed for obtaining the number and location of new rain gauges in the ‘blank’ monitoring region, respectively. Two new rain gauges in the ‘blank’ monitoring region were identified. One rain gauge was located near the Anxi hydrological station and the other was located in the lower reaches of Anxi sub-basin, respectively. The locations of the two new rain gauges were proven to be reasonable. The number of optimal rain gauges in the Jinjiang Basin was increased to 13. The method proposed in this study provides a novel and simple approach to solve the problems of redundant rain gauges and blank monitoring areas in rain gauge networks. This method is beneficial for improving the optimization level of rain gauge networks and provides a reference for such an optimization.

Warming in mountainous areas has obvious elevation dependence (warming rate increases with elevation), which deeply impacts runoff change in mountainous areas. This study analysed the influence of elevation-dependent warming on runoff in the headwater region of the Urumqi River Basin (URB) based on meteorological data, remote sensing images, and runoff data. Results indicated a significant warming rate in the URB from 1960 to 2019 (0.362 °C/decade; p < 0.01). The temperature increased with an obvious elevation-dependent warming in the URB, especially during winter. Glaciers sharply retreated in the headwater region of the URB under regional warming, and remote-based results showed that glacier areas decreased by 29.45 km² (−57.81%) from the 1960s to 2017. The response of glacier mass balance and meltwater runoff to temperature change has a lag of 3 years in the headwater region of the URB. The elevation-dependent warming of temperature changes significantly impacted glacial meltwater runoff in the URB (R² = 0.49). Rising temperatures altered the glacial meltwater runoff, and the maximum annual runoff of the Urumqi Glacier No. 1 meltwater runoff increased 78.6% in 1990–2017 compared to 1960–1990. During the period of 1960–1996, the total glacial meltwater runoff amounted to 26.9 × 10⁸ m³, accounting for 33.4% of the total runoff during this period, whereas the total glacial meltwater runoff accounted for 51.1% of the total runoff in 1996–2006. Therefore, these results provide a useful reference for exploring runoff changes in mountainous watersheds in the context of elevation-dependent warming.