Urban overheating has become a global issue, exacerbated by climate change and leading to serious risks for public health and urban sustainability. Traditional methods, such as numerical simulations and field measurements, often face challenges due to uncertainties in input data. This study predicts the longevity and severity of future urban overheating by integrating field measurements with machine learning (ML) models, focusing on the impact of urban greening under different global warming (GW) scenarios. Field measurements were conducted from June 15 to September 14, 2024, at an office campus in Ottawa (a cold climate zone). Microclimate data was collected at four locations featuring distinct vegetation coverage: a large lawn area without trees (Lawn), a parking plot with no greening (Parking), a greenery area with sparsely distributed trees (Tree), and a forested area with 100 % tree coverage (Forest). Models-including Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) networks-were trained on local climate data, with LSTM demonstrating superior accuracy. Four GW scenarios aligned with Shared Socioeconomic Pathways for 2050 and 2090 were examined. Results show that the Universal Thermal Climate Index (UTCI) at the Parking plot could increase from about 27 degrees C under GW1.0 to 31 degrees C under GW3.5. Low health risk (UTCI > 26 degrees C) is projected to rise at all sites, while dense tree coverage effectively prevents extremely high-risk conditions (UTCI > 38.9 degrees C). These findings underscore the importance of urban greening in mitigating severe thermal stress and enhancing outdoor comfort under future climates.

The thresholds and influencing factors that trigger the transition between meteorological drought and soil drought are poorly understood, which hinders the effective establishment of drought early warning systems and preventive measures. We selected a typical inland river basin with an alpine-oasis-desert ecosystem, the Shiyang River Basin in Northwest China, toidentify the drought events from 1980 to 2020, and then used the Copula function to assess the risk and threshold of the propagation from meteorological drought to soil drought. The Drought Intensity Propagation Index (DIP) and the Drought Duration Propagation Index (DDP) were used to quantify the propagation process of drought characteristics. Furthermore, we identified and quantified the drivers of drought propagation characteristics. The results showed that: (1) the duration of meteorological drought events extended in 85% of the regions and the intensity of drought increased in 26% of the areas from 1980 to 2020, while the duration of soil drought events decreased and the intensity of drought increased in almost all regions; (2) the propagation of drought has spatial heterogeneity, that is, from the upper mountain to the lower oasis, the probability of propagation from meteorological drought to soil drought decreased, the degree of drought intensity propagation weakened, and the degree of drought duration propagation intensified; (3) the cumulative precipitation deficit thresholds triggering soil drought were -11mm/ month, -7.5mm/ month and -5mm/ month, respectively; (4) precipitation and evapotranspiration were the main driving factors of the drought propagation characteristics, which explained 44% of the drought intensity propagation upstream and explained 60% of the drought duration propagation downstream, respectively. This study further reveals the law and mechanism of drought propagation, which is helpful for the early monitoring and warning of drought events in river basins, and provides new ideas for the construction of drought propagation models.

The source region of the Yellow River Basin (SYRB) is an alpine region sensitive to climate changes. As global climate change intensifies, it is essential to explore the future hydrological regime alteration and its ecological effects on the basin to provide evidence for water resources management and ecological restoration in the area. This study offers a framework for assessing the future multi-dimensional hydrological regime alteration and its ecological effects. Selected from Global Climate Models (GCMs) that performed well in the Sixth Coupled Model Intercomparison Project (CMIP6), the VIC model was forced by the GCMs after downscaling and bias-correction. Then the hydrological regime alteration and potential ecological effects in the region under SSP126, SSP245, SSP585 future scenarios were systematically assessed by the IHA-RVA system, and the contribution of different uncertainty sources was quantified through the two-way ANOVA. The results indicated that: (1) The temperature and precipitation will increase in the future under all scenarios, while the runoff tends to show a downward trend. By 2015-2100, the flow under SSP126, SSP245 and SSP585 scenarios may vary from-44.7 to 13.0 %,-48.7 to 2.43 %,-53.7 to 4.0 %, respectively, when compared to the reference period (1961-1990). (2) Moderate alterations with RVA = 33 % will be observed in the hydrological regime in the basin under SSP245 and SSP585 scenarios, and low alterations with RVA = 26.7 % under the SSP126 scenario. The spring discharge of the basin will decrease significantly, and the annual extreme flow is projected to decrease, but the frequency of drought and flood events will tend to increase. (3) There are multiple sources of uncertainty in predicting the future hydrological regime in the SYRB. The uncertainty due to the climate model is the dominant factor, fol-lowed by the interaction between emission scenarios and the climate model, and the average contribution to uncertainty of them is 69.5 % and 20.8 % respectively. The uncertainty of emission scenarios is mainly reflected in winter flow, while the climate model mainly affects the summer flow and annual maximum flow, and the uncertainty of interaction between them varies in different periods.

The Lancang-Mekong River Basin (LMRB) has been particularly vulnerable to serious and successive droughts during the last decades. Nevertheless, the characteristics of spatiotemporal variations in meteorological drought (MD) and hydrological drought (HD) with an emphasis on drought propagation have yet to be thoroughly investigated. In this study, based on reanalysis data, the standardized precipitation index (SPI) and standardized runoff index (SRI) were employed to comprehensively examine the evolution and propagation characteristics of MD and HD. Spearman rank correlation and wavelet analyses were employed for the correlation and propagation of MD to HD. The influencing factors of the drought propagation time (DPT) were also explored. The results indicate that: (1) During 1950-2021, the regions with worsening drought conditions were primarily in the Lancang River Basin in Yunnan Province, the highlands in north Laos, and the Khorat Plateau in Thailand. (2) The drought characteristics varied significantly on different timescales. For the longer timescales, the MDs and HDs corresponded to fewer drought events but longer durations and larger severities. The average duration and severity of HD were higher than MD on all selected timescales. (3) The DPTs from MD to HD exhibited noticeable spatial variability ranging from 2 to 9 months. In addition, there were statistically positive correlations between the MD and HD in each sub-region. (4) Precipitation was the dominant factor influencing the spatial distribution of DPTs at the basin scale, while the catchment properties, represented by the land use and elevation, had nonnegligible influences on the DPTs. (5) Human activities weakened the correlations between MD and HD. Meanwhile, the DPTs were prolonged as a result of human influence. Our findings highlight the characteristics of HD response to MD in the diverse sub-regions of the LMRB and provide crucial information for early warning and improvement of drought resilience in this transboundary international river.

Evaluating the impact of multi-source uncertainties in complex forecasting systems is essential to understanding and improving the systems. Previous studies have paid little attention to the influence of multi-source uncertainties in complex meteorological and hydrological forecasting systems. In this study, we developed a general ensemble framework based on Bayesian model averaging (BMA) for evaluating the impact of multi-source uncertainties in complex forecast systems. Based on this framework, we used eight numerical weather prediction products from the International Grand Global Ensemble (TIGGE) dataset, four hydrological models with different structures, and 1000 sets of parameters to comprehensively account for the input, structure, and parameter uncertainties. The framework’s application to the Chitan Basin in China revealed that the numerical weather prediction input uncertainty in the forecasting system was more significant than the hydrological model uncertainty. The hydrological model structure uncertainty was more prominent than the parameter uncertainty. The accuracy of the numerical weather prediction dominates the accuracy of the forecast of high flows. In addition, the structures and parameters of the hydrological model and their interactions contributed to the main uncertainty of the low flow forecasts. The streamflow was more realistically represented when the three uncertainty sources were considered jointly. By accounting for the significant uncertainty sources in complex forecast systems, the BMA ensemble forecasting produces more realistic and reliable predictions and reduces the influences of other incomplete considerations. The developed multi-source uncertainty assessment framework improves our understanding of the complex meteorological and hydrological forecasting system. Therefore, the framework is promising for improving the accuracy and reliability of complex forecasting systems.

Reference evapotranspiration (ET0), as one important variable in climatology, hydrology, and agricultural science, plays an important role in the terrestrial hydrological cycle and agricultural irrigation. However, the ET0 estimation process is inaccurate due to the lack of weather stations and historical data. In this study, a new method of ET0 estimation was proposed to improve the ET0 estimation performance in regions with limited data. Four empirical models with different data requirements, Albrecht, Hargreaves-Samani, Priestley-Taylor, and Penman, were applied and optimized the parameters by the Shuffled Complex Evolution-University of Arizona algorithm with the ET0 calculated by the Penman-Monteith model as the reference value at 600 meteorological stations in China. Two machine learning models, Random Forest (RF) and Multiple Linear Regression (MLR) were used to establish the regionalization of the parameter of the empirical model. The result showed that parameter optimization could significantly improve ET0 estimation in different climate regions of China. The Penman model has the strongest physical foundation and the highest estimation accuracy, followed by the Hargeaves-Samani and Priestley-Taylor model. The mass-transfer-based model, Albrecht, could only estimate regional ET0 efficiently after parameter optimization. Based on the more advanced RF machine learning regionalization method that considers complex linear relationships of variables, ET0 estimation in regions lacking data could be improved efficiently. Machine learning could be used to describe the ET0 model parameters in different regions because of the similarity. The combination of machine learning and empirical model could provide a new method for ET0 estimation in data deficient regions.

In the context of global warming and increasing human activities, the acceleration of the water cycle will increase the risk of basin drought. In this study, to analyze the spatial and temporal evolution characteristics of hydrological and meteorological droughts over the Hanjiang River Basin (HRB); the Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI) were selected and applied for the period 1961–2018. In addition, the cross-wavelet method was used to discuss the relationship between hydrological drought and meteorological droughts. The results and analysis indicated that: (1) the meteorological drought in the HRB showed a complex cyclical change trend of flood-drought-flood from 1961 to 2018. The basin drought began to intensify from 1990s and eased in 2010s. The characteristics of drought evolution in various regions are different based on scale. (2) During the past 58 years, the hydrological drought in the HRB has shown a significant trend of intensification, particularly in autumn season. Also, the hydrological droughts had occurred frequently since the 1990s, and there were also regional differences in the evolution characteristics of drought in various regions. (3) Reservoir operation reduces the frequency of extreme hydrological drought events. The effect of reducing the duration and intensity of hydrological drought events by releasing water from the reservoir is most obvious at Huangjiagang Station, which is the nearest to Danjiangkou Reservoir. (4) The hydrological drought and meteorological drought in the HRB have the strongest correlation on the yearly scale. After 1990, severe human activities and climate change are not only reduced the correlation between hydrological drought and meteorological drought in the middle and lower reaches of the basin, but also reduced the lag time between them. Among them, the hydrological drought in the upper reaches of the basin lags behind the meteorological drought by 1 month, and the hydrological drought in the middle and lower reaches of the basin has changed from 2 months before 1990 to 1 month lagging after 1990.

The Yellow River Basin is an important economic belt and key ecological reservation area in China. In the context of global warming, it is of great significance to project the drought disaster risk for ensuring water security and improving water resources management measures in practice. Based on the five Global Climate Models (GCMs) projections under three scenarios of the Shared Socioeconomic Pathways (SSP) (SSP126, SSP245, SSP585) released in the Sixth Coupled Model Intercomparison Project (CMIP6), this study analyzed the characteristics of meteorological drought in the Yellow River Basin in combination with SPEI indicators over 2015–2100. The result indicated that: (1) The GCMs from CMIP6 after bias correction performed better in reproducing the spatial and temporal variation of precipitation. The precipitation in the Yellow River Basin may exhibit increase trends from 2015 to 2100, especially under the SSP585 scenario. (2) The characteristics of meteorological drought in the Yellow River Basin varied from different combination scenarios. Under the SSP126 scenario, the meteorological drought will gradually intensify from 2040 to 2099, while the drought intensity under SSP245 and SSP585 scenarios will likely be higher than SSP126. (3) The spatial variation of meteorological drought in the Yellow River Basin is heterogeneous and uncertain in different combination scenarios and periods. The drought tendency in the Loess Plateau will increase significantly in the future, and the drought frequency and duration in the main water conservation areas of the Yellow River Basin was projected to increase.

We evaluated 24-h control forecast products from The International Grand Global Ensemble center over the 10 first-class water resource regions of Mainland China in 2013–18 from the perspective of precipitation processes (continuous) and precipitation events (discrete). We evaluated the forecasts from the China Meteorological Administration (CMA), the Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), the Japan Meteorological Agency (JMA), the Korea Meteorological Administration (KMA), the United Kingdom Met Office (UKMO), and the National Centers for Environmental Prediction (NCEP). We analyzed the differences among the numerical weather prediction (NWP) models in predicting various types of precipitation events and showed the spatial variations in the quantitative precipitation forecast efficiency of the NWP models over Mainland China. Meanwhile, we also combined four hydrological models to conduct meteo-hydrological runoff forecasting in three typical basins and used the Bayesian model averaging (BMA) method to perform the ensemble forecast of different scenarios. Our results showed that the models generally underestimate and overestimate precipitation in northwestern China and southwestern China, respectively. This tendency became increasingly clear as the lead time rose. Each model has a high reliability for the forecast of no-rain and light rain in the next 10 days, whereas the NWP model only has high reliability on the next day for moderate and heavy rain events. In general, each model showed different capabilities of capturing various precipitation events. For example, the CMA and CMC forecasts had a better prediction performance for heavy rain but greater errors for other events. The CPTEC forecast performed well for long lead times for no-rain and light rain but had poor predictability for moderate and heavy rains. The KMA, UKMO, and NCEP forecasts performed better for no-rain and light rain. However, their forecasting ability was average for moderate and heavy rain. Although the JMA model performed better in terms of errors and accuracy, it seriously underestimated heavy rain events. The extreme rainstorm and flood forecast results of the coupled JMA model should be treated with caution. Overall, the ECMWF had the most robust performance. Discrepancies in the forecasting effects of various models on different precipitation events vary with the lead time and region. When coupled with hydrological models, NWP models not only control the accuracy of runoff prediction directly but also increase the difference among the prediction results of different hydrological models with the increase in NWP error significantly. Among all the single models, ECMWF, JMA, and NCEP have better effects than the other models. Moreover, the ensemble forecast based on BMA is more robust than the single model, which can improve the quality of runoff prediction in terms of accuracy and reliability.

干旱是一种最常见的自然灾害,随着人类活动和气候变化的加剧,干旱事件的发生愈发频繁,对人类的生产生活产生了巨大影响.基于1961～2018年汉江流域0.25°×0.25°格点降水资料,选用标准化降水指数(SPI)定量分析了汉江流域的月尺度、季尺度、年尺度气象干旱的干旱趋势、干旱频率及干旱强度,揭示了汉江流域气象干旱发生的时空演变特征.结果表明:(1)SPI值能较好的反映汉江流域气象干旱变化特征,随着时间尺度的增加,SPI值波动幅度减小,稳定性增强.月、季、年尺度SPI序列的突变年份分别为1980、1988、1994年,季尺度和年尺度SPI序列分别表现出2和4年的显著周期性特征.(2)汉江流域自20世纪90年代以来呈现中部地区干旱化、东部和西部地区湿润化的趋势,干旱发生频率总体呈上升趋势,干旱强度呈现中部高,东西低的特征.其中丹江口水库附近区域和唐白河下游段呈显著干旱化趋势,丹江口以上区域干旱频率最高,干旱强度最大,轻旱、中旱事件频发.(3)各地区的季节性气象干旱特征具有一定差异性.丹江口以下地区秋旱趋势最显著,唐白河地区夏旱发生频率最高,且以轻旱、中旱事件为主,丹江口以上地区秋旱强度最高.

Choosing an appropriate GCM (Global Climate Model, GCM) is of great significance for the simulation of the hydrological cycle over a basin under future climate scenarios. In this study, the Rank Score Method (RS) with eight indicators were applied to comprehensively evaluate the suitability of 19 GCMs issued in the Sixth Global Atmosphere and Coupled Model Intercomparison Project (CMIP6) to the Yellow River Basin (YRB). The results indicated that: 1) The GCMs perform differently in simulating precipitation over the YRB with the top six GCMs ranking from MRI-ESM2-0, ACCESS-CM2, CNRM-CM6-1, CNRM-ESM2-1, FGOALS-f3-L, to MPI-ESM1-2-HR. 2) Most GCMs overestimated the precipitation, and poorly simulated the phase distribution of extremes mainly due to overstimulation of wet season span and precipitation amount in the season, although all GCMs could capture decadal feature of annual precipitation. Meanwhile, it is also found that most GCMs underestimated summer precipitation and overestimated spring precipitation. 3) The GCMs well simulated the spatial distribution of annual precipitation, with an overestimation in the source area, and an underestimation in the northern part of the middle reaches of YRB.

枯季是水旱、水生态和水资源问题的重要时期,枯季径流的变化直接影响着河流生态和流域水资源管理.基于中国网格气象数据和主要江河枯季径流资料,初步分析了1961—2018年中国气候变化趋势和主要江河枯季径流演变特征与成因.结果表明,全国枯季平均气温显著上升,北方地区升温较早,南方地区2001—2018年升温明显.全国约84％的地区枯季降水有增加趋势,其中约42.2％的地区增加显著;全国枯季降水呈现西北、东北和东南显著增加,中部变化不显著格局.黄河中游和海河枯季径流下降显著,2001—2018年黄河中游枯季径流较1961—1980年减少了34％,同时期海河流域枯季径流量减少幅度均超过80％;松花江上游和长江流域枯季径流增加显著,2001—2018年松花江上游枯季径流量增加了约67％,长江流域枯季径流量增加了约16％.枯季降水增加主导了松花江上游、辽河、淮河、长江以及珠江枯季径流的增加;气温的显著上升对黄河中游和海河等地枯季径流有显著负向作用;人类活动是松花江中游、黄河和海河枯季径流下降的主要影响因素.尽管全国枯季降水的增加对于缓解流域生态和水资源问题有积极作用,但人类活动和气温显著上升加速了水资源的消耗,加大了流域水资源脆弱性.