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Evapotranspiration (ET) is a crucial component of the terrestrial hydrological cycle. Latent heat flux (LE, equivalent to ET in W/m2) observed by the eddy covariance (EC) technique, as known as LEEC, has been publicly recognized as highly accurate benchmark for global ET estimation. Currently, there is an increasing need for long time-series benchmark data to support climate change analysis, construction of new models, and validation of new products. However, existing LEEC datasets, like FLUXNET2015, face significant challenges due to limited observation periods and extensive data gaps. This hinders their application. To address these issues, we developed a gap-filling and prolongation framework for LEEC data and established a benchmark dataset for global ET estimation from 2000 to 2022 across 64 sites at various time scales. The framework mainly contained 3 parts: site selection and data pre-processing, gap-filled half-hourly / hourly LE data generation, and prolonged daily LE data generation. We selected 64 sites from FLUXNET2015 based on a rigorous filtering criterion. A novel bias-corrected random forest (RF) algorithm was used as the gap-filling and prolongation algorithm of the framework to produce seamless half-hourly and daily LE data. After analysis, the framework using novel bias-corrected RF algorithm achieves excellent performance both in hourly gap-filling and daily prolongation, with a median RMSE of 32.84 W/m2 and 16.58 W/m2, respectively. The algorithm significantly improved the gap-filling performance for long gaps and extreme values compared with the original RF and marginal distribution sampling (MDS) algorithm. The results demonstrate robust prolongation performance of our framework both on prolonging directions and temporal stability. There is a high consistency in data distribution between our gap-filled dataset and FLUXNET2015 dataset. In conclusion, a benchmark dataset for global ET estimation based on FLUXNET2015 from 2000 to 2022 was firstly published. This dataset can strongly provide data support for ET modelling, water-carbon cycle monitoring and climate change analysis. It is made freely available via the following repository: https://doi.org/10.5281/zenodo.13853409 (Li et al., 2024b). [ABSTRACT FROM AUTHOR]

Estimating runoff components, including surface flow, baseflow and total runoff is essential for understanding precipitation partition and runoff generation and facilitating water resource management. However, a general framework to quantify and attribute runoff components is still lacking. Here, we propose a general formulation through observational data analysis and theoretical derivation based on the two-stage Ponce-Shetty model (named as the MPS model). The MPS model characterizes mean annual runoff components as a function of available water with one parameter. The model is applied over 662 catchments across China and the contiguous United States. Results demonstrate that the model well depicts the spatial variability of runoff components with R 2 exceeding 0.81, 0.44 and 0.80 for fitting surface flow, baseflow and total runoff, respectively. The model effectively simulates multi-year runoff components with R 2 exceeding 0.97, and the proportion of runoff components relative to precipitation with R 2 exceeding 0.94. By using this conceptual model, we elucidate the responses of surface flow and baseflow to available water and environmental factors for the first time. The surface flow is jointly controlled by precipitation and environmental factors, while baseflow is mainly influenced by environmental factors in most catchments. The universal and concise MPS model offers a new perspective on the long-term catchment water balance, facilitating broader application in large-sample investigations without complex parameterizations and providing an efficient tool to explore future runoff variations and responses under changing climate. [ABSTRACT FROM AUTHOR]

Recent studies have suggested that drought projections using Palmer drought severity index (PDSI) and standardized evapotranspiration precipitation index (SPEI) may overestimate drought severity. This overestimation occurs because the potential evapotranspiration (PET) calculations fail to consider the interactive effects of vegetation responses such as increased leaf area index (LAI) and constrained stomatal conductance, which are influenced by elevated atmospheric CO2 concentrations ([CO2]). To address this issue, our study replaced the traditional Penman‐Monteith (PM) equation with a recently proposed PET equation that includes the effects of changing [CO2] and LAI to assess droughts at monthly scale in the Upper Yangtze River basin, which experiences the vegetation greening. The findings indicated a consistent increasing trend in drought conditions with minimal discrepancy between the two equations over the historical period (1986–2017). This consistency arises because the water‐saving effects of increased [CO2] and the greening effects of rising LAI largely counterbalance each other. However, for the future period (2018–2100), projections using PM equation predicted an intensification of drought conditions. In contrast, the improved SPEI indicated no significant drought variations, and the improved PDSI suggested a wetting trend. This divergence can be attributed to the water‐saving effects increasingly outweighing the greening effects, as PET shows a decreasing sensitivity to LAI with LAI increasing, but maintains a near‐constant sensitivity to elevated [CO2]. Consequently, the indices based on PM equation tend to overestimate future drought severity. Overall, this study demonstrates that the new PET estimation method is more capable of responding to the changing environment. Plain Language Summary: This study enhances drought projections by incorporating the effects of elevated atmospheric CO2 concentrations ([CO2]) and increased vegetation, as indicated by the leaf area index (LAI), on potential evapotranspiration (PET). Historically, the water‐saving effect of elevated [CO2] and the greening effect due to increased vegetation have counterbalanced each other, resulting in minimal discrepancies between traditional and newly developed methods. However, our innovative approach demonstrates that with rising LAI, the sensitivity of PET to LAI diminishes, while its sensitivity to CO2 remains relatively constant. This finding suggests that in future scenarios with concurrent increases in [CO2] and LAI, the water‐saving effect will potentially dominate the greening effect, leading traditional methods to overestimate future drought severity. Consequently, this new method offers a more accurate framework for predicting changes in future environmental conditions. Key Points: Palmer drought severity index (PDSI) and standardized evapotranspiration precipitation index (SPEI) are improved using a newly proposed potential evapotranspiration equation that considers the effects of atmospheric CO2 concentrations and leaf area index (LAI) changeThe improved PDSI and SPEI are more applicable in a changing environment and characterize the spatial differences of vegetation variationThe impacts of increased atmospheric CO2 concentrations and LAI must be factored into the assessment of meteorological drought trends [ABSTRACT FROM AUTHOR]

The Priestley–Taylor (PT) coefficient (α) is generally set as a constant value or is fitted as an empirical function of environmental variables, and it can bias the evaporation estimation or hydrological projections under global warming. By using an atmospheric boundary layer model, this study derives a theoretical and parameter-free equation for estimating α as a function of air temperature (T) and specific humidity (Q). With observations from several waterbodies and non-water-limited land sites, we demonstrate that, in addition to estimating the value of α well, the derived expressions can also capture the sensitivity of α to T and Q , that is, dα/dT and dα/dQ. α is generally negatively associated with T and Q , in which regard T plays a more fundamental role in controlling α behaviors. Based on climate model data, we further show that this negative relationship between α and T is of great importance for long-term hydrological predictions. We also provide a lookup graph for practical and broad uses to directly find the values of dα/dT and dα/dQ under specific conditions. Overall, the derived expression gives a physically clear and straightforward approach to quantify changes in α , which is essential for PT-based hydrological simulation and projections. [ABSTRACT FROM AUTHOR]

The hydrogel actuated membrane in this paper is highly stretchable while having an electrically responsive output force. At a doping level of 0.6 g SA, the elongation at break of the hydrogel-driven membrane reached 3117% and the output force density reached 16.49 mN/g. In addition, the elastic modulus, tensile strength, and specific capacitance of the driving membrane reached the maximum values of 29.05 kPa, 907.24 kPa, and 452.8 mF/g at SA doping content of 0.75 g. [ABSTRACT FROM AUTHOR]

Volumetric changes induced by soil moisture phase changes can lead to pore system redistribution in freezing and thawing soil, which in turn affects soil strength and stability. The prefreezing water content and the number of freeze–thaw cycles (FTCs) affecting key factors of soil pore changes, and they determine the volumetric change magnitude and frequency during ice–water phase transitions. This study aims to reveal the effect of the prefreezing water content and the number of freeze–thaw cycles on the pore size distribution (PSD) of black soil, meadow soil and chernozem, which account for the largest arable land area in Heilongjiang Province, China. In situ soil samples with different prefreezing water contents were subjected to 1, 2, 3, 5, 10, and 20 FTCs, and then nuclear magnetic resonance (NMR) was used to quantify the PSD. It was shown that the pore sizes of the three soil types spanned multiple orders of magnitude, ranging from 0.001 to 100 μm overall. The inflection point of the cumulative porosity curves of all three soils occurred near 0.1 μm. For black soil and chernozem with high prefreezing water contents, when the number of FTCs reached 10 or 20, the soil self-weight led to thaw settlement, which reduced the difference in the total porosity of the soils with varying moisture contents. The initial FTC exerts the most significant influence on the pore structure. The impact of the prefreezing water content on soil pore structure diminishes as the number of FTCs increases. The plant root residues rendered meadow soil less sensitive to water content differences after the first FTCs but also limited the development of macropores during the late freeze–thaw period. The prefreezing water content alters the distribution of soil moisture before freezing and has a greater influence on the pore distribution of frozen-thawed soils compared to the cumulative effect of multiple FTCs. [ABSTRACT FROM AUTHOR]