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With global climate change and irrational human activities, regional water resource conflicts are becoming more and more pronounced. The availability of water resource in watersheds can be indicated by the water yield. Exploring the factors that influence the water yield is crucial in responding to climate change and protecting water resource. Previous research on the factors influencing the water yield has frequently adopted a macro-level perspective, which has failed to reflect the influencing mechanisms of changes at the local scale adequately. Therefore, this study proposes a novel framework for integrally evaluating the impacts of climate change and human activities on water yield services from both local and global perspectives. Taking Ganzhou City, the source of the Ganjiang River, as an example, the results show the following: (1) Ganzhou City had the largest water yield of 1307.29 mm in 2016, and the lowest was only 375.32 mm in 2011. The spatial distribution pattern was mainly affected by the surface environment, and the high-value water yield regions in the study area were predominantly located in urban areas with flat terrain. (2) At the local scale, regions where human activities contribute more than 80% accounted for 25% of the area. In comparison, the impact of climate change accounted for 0.95%. The contribution rate of human activities to the water yield in Ganzhou City was significantly greater than that of climate change. (3) At the global scale, the simulation results of four scenarios show that climate change contributed (>98%) to the water yield, which is significantly higher than human activities (<2%). This study puts forward pioneering views on the research of water yield driving forces and provides a valuable theoretical basis for water resource protection and ecological environment construction. [ABSTRACT FROM AUTHOR]

Satellite‐based thermal infrared (TIR) land surface temperature (LST) is hindered by cloud cover and is applicable solely under clear‐sky conditions for estimating surface urban heat island intensity (SUHII). Clear‐sky SUHII may not accurately represent all‐sky conditions, potentially introducing quantitative biases in assessing urban heat islands. However, the differences between clear‐sky and all‐sky SUHIIs and their spatiotemporal variations are still poorly understood. Our analysis of over 600 global cities demonstrates that clear‐sky SUHII is mostly higher than all‐sky SUHII, particularly in summer, daytime, and precipitation‐rich regions. Besides, clear‐sky SUHII typically exhibits stronger seasonal and diurnal contrasts than all‐sky SUHII, especially for cities located in humid regions. These discrepancies can be attributed mainly to the increased missing LST data caused by cloud enhancement in urban areas. Our findings highlight the tendency for clear‐sky observations to overestimate SUHII, providing valuable insights for standardizing the quantification of surface urban heat islands. Plain Language Summary: Surface urban heat island intensity (SUHII) and its spatial and temporal variations are important for describing the urban thermal environment. SUHII is usually estimated from remotely sensed land surface temperature (LST), which is only available under clear‐sky conditions. The SUHII derived from clear‐sky observations may differ from the SUHII under all‐sky conditions. However, there is currently a lack of large‐scale quantitative assessments addressing the differences between clear‐sky and all‐sky SUHIIs. This study fills this research gap and indicates a substantial overestimation of SUHII in humid regions when using clear‐sky LST. This overestimation can be explained by the increased occurrence of missing LST data caused by the enhanced presence of clouds in urban areas. Our findings show the importance of utilizing all‐sky LST data in the examination of urban surface thermal environments, especially for cities situated in humid regions. Key Points: Clear‐sky surface urban heat island intensity (SUHII) shows higher values and stronger spatiotemporal variations than all‐sky SUHII, notably in summer, daytime, and humid areasThe annual daytime SUHII for tropical cities is, on average, overestimated by 30% when relying on clear‐sky land surface temperature (LST) observationsDifferences in clear‐sky and all‐sky SUHIIs can be explained by more missing LST data caused by increased clouds in urban areas [ABSTRACT FROM AUTHOR]

The urban heat island (UHI) effect, a phenomenon of local warming over urban areas, is the most well-known impact of urbanization on climate. Globally consistent estimates of the UHI intensity (UHII) are crucial for examining this phenomenon across time and space. However, publicly available UHII datasets are limited and have several constraints: (1) they are for clear-sky surface UHII, not all-sky surface UHII and canopy (air temperature) UHII; (2) the estimation methods often neglect anthropogenic disturbance, introducing uncertainties in the estimated UHII. To address these issues, this study proposes a new dynamic equal-area (DEA) method that can minimize the influence of various confounding factors on UHII estimates through a dynamic cyclic process. Utilizing the DEA method and leveraging various gridded temperature data, we develop a global-scale (>10,000 cities), long-term (over 20 years by month), and multi-faceted (clear-sky surface, all-sky surface, and canopy) UHII dataset. Based on these estimates, we provide a comprehensive analysis of the UHII and its trends in global cities. The UHII is found to be greater than zero in >80% of cities, with global annual average magnitudes around 1.0 °C (day) and 0.8 °C (night) for surface UHII, and close to 0.5 °C for canopy UHII. Furthermore, an interannual upward trend in UHII is observed in >60% of cities, with global annual average trends exceeding 0.1 °C/decade (day) and over 0.06 °C/decade (night) for surface UHII, and slightly surpassing 0.03 °C/decade for canopy UHII. Notably, there exists a positive correlation between the magnitude and trend of UHII, suggesting that cities with stronger UHII tend to experience faster growth in UHII. Additionally, discrepancies in UHII are found between different temperature data, stemming not only from distinctions in data types (surface or air temperature) but also from differences in data acquisition times (Terra or Aqua), weather conditions (clear-sky or all-sky), and processing methodologies (with or without gap filling). Overall, our proposed method, dataset, and analysis results have the potential to provide valuable insights for future urban climate studies. The UHII dataset is publicly available at https://doi.org/10.6084/m9.figshare.24821538. • A new dynamic equal-area (DEA) method is proposed to estimate UHII. • Our UHII dataset spans over 20 years and covers 10,000+ global cities. • Our UHII dataset includes surface (clear-sky and all-sky) and canopy UHIIs. • The trend of UHII is found to be positively correlated with its magnitude. [ABSTRACT FROM AUTHOR]

The estimation of surface urban heat island intensity (SUHII) is crucial for studying the urban thermal environment, which is influenced not only by the commonly known definition of rural reference but also by the delineation of urban extent. Existing studies relies on various urban extent products defined in different ways, and the influence of urban extent discrepancy (UED) on SUHII estimates still remains unclear. In this study, we collected five open-source global urban extent products (GUEPs) for the year 2015 and corresponding daily land surface temperature (LST) observations (MYD11A1). Based on these products, we quantified the UED-induced uncertainty in SUHII estimates by comparing absolute difference (ΔSUHII AD) and relative difference (ΔSUHII RD) in SUHII among GUEPs across 892 global cities. Besides, we introduced an ISF-constrained (ISF–C) method to reduce SUHII differences among GUEPs by constraining the impervious surface fraction (ISF) within urban and rural extents. The results show that urban extents delineated by different GUEPs are not consistent, leading to their difference in ISF as well as LST, which in turn causes uncertainties in the estimated SUHII. On average for global cities, the annual daytime and nighttime ΔSUHII AD are 0.46 ± 0.02 °C (mean ± 95% confidence interval) and 0.24 ± 0.01 °C, with corresponding ΔSUHII RD of 42.0 ± 2.7% and 35.2 ± 2.3%, respectively. The UED-induced uncertainty in SUHII estimates varies among climate zones, and the annual daytime ΔSUHII RD averaged for cities located in the arid zone reaches up to 60.8 ± 6.6%, which is nearly twice as high as that in other climate zones. More importantly, both ΔSUHII AD and ΔSUHII RD show lower values when using the ISF-C method, implying the effectiveness of this method. This study highlights the non-negligible impact of UED on the estimation of SUHII, which requires more attention due to the inconsistency of urban extents among current products. [Display omitted] • Existing global products differ greatly in their delineated urban extents. • Urban extent discrepancy (UED) brings great uncertainty to the SUHII estimates. • The UED-induced uncertainty in annual daytime SUHII averagely reaches ∼40%. • Arid cities witness larger UED-induced uncertainty in SUHII estimates. • A simple but effective method is proposed to reduce the UED-induced uncertainty. [ABSTRACT FROM AUTHOR]

The urban heat island (UHI) effect has attracted great attention due to its potential impacts on rapidly growing urban areas. Using remotely sensed estimates of land surface temperature (LST), a large number of studies have focused on the surface UHI (SUHI) effect, which can be characterized by its two fundamental properties: intensity and footprint. The SUHI intensity reflects the LST difference between the urban area and the background reference area (BRA), and the SUHI footprint indicates the spatial extent influenced by the heat island. Currently, numerous methods have been developed to estimate the SUHI intensity and footprint, but are still greatly challenged by three main issues. Namely, the discrepancy in BRA selection criterion brings great uncertainty to the estimated SUHI intensity, the estimation of SUHI footprint is largely constrained by the predefined models, and the quantification of SUHI effect is potentially influenced by several confounding factors. Here, we proposed an adaptive synchronous extraction (ASE) method, which is capable of adaptively selecting the most optimal BRA while removing the influence of confounding factors, and achieving synchronous estimation of SUHI intensity and footprint. We applied the ASE method to 254 North American cities and conducted an in-depth comparative analysis to discuss its applicability and benefits. The main results include: (1) The ASE method avoids the limitations of existing methods in BRA selection and model presetting, and shows resilience to parameter variations. This makes the ASE method highly applicable to quantify the SUHI intensity and footprint in cities with various thermal characteristics. (2) The ASE method can better highlight the spatial, seasonal and day-night contrasts in the estimated SUHI intensity. This superiority is particularly evident when comparing it to methods based on the equal-area buffer or the simplified urban-extent algorithm. (3) Confounding factors pose non-negligible impacts on the quantification of the SUHI effect. Typically, ignoring the influence of topographic relief or missing LST data can lead to an overall overestimation of the SUHI intensity, while not removing surrounding urban areas will cause some underestimation of the SUHI intensity. Overall, the proposed ASE method provides a new generalizable tool for quantifying the SUHI effect, which has great potentials for future studies and urban climate assessments. • The ASE method can adaptively and dynamically select the background reference area. • The ASE method achieves simultaneous estimation of the SUHI intensity and footprint. • Comparative analysis shows the reliability and benefits of the ASE method. • Confounding factors pose non-negligible impacts on the quantification of SUHI effect. [ABSTRACT FROM AUTHOR]